The document describes Johnson's algorithm for finding shortest paths between all pairs of vertices in a sparse graph. It discusses how the algorithm uses reweighting to compute new edge weights that preserve shortest paths while making all weights nonnegative. It shows how Dijkstra's algorithm can then be run on the reweighted graph to find shortest paths between all pairs of vertices. The key steps are: (1) adding a source node and zero-weight edges, (2) running Bellman-Ford to compute distances from the source, (3) using these distances to reweight the edges while preserving shortest paths, resulting in nonnegative weights.
These slides have full understanding about Equivalent Moore Mealy... Having Moore to Mealy conversion and Mealy to Moore conversion...
These slides also describing the concept of Transducers as models of sequential circuits (both w.r.t Moore and Mealy)...
All these concepts are explained with easy examples...
The document describes an opportunity for students to participate in Terakki's first Junior Model United Nations committee. The JMUN aims to teach students about global issues, public speaking skills, and experience real debates. Students would take on the role of delegates representing different UN member states as they research issues, debate in committee meetings, and work to pass resolutions. Key skills learned would include research, presentation, debating, and developing global awareness.
This document outlines several modern philosophies of education including progressivism, reconstructionalism, experimentalism, existentialism, and perennialism. Progressivism focuses on making education relevant to students' interests and experiences. Reconstructionalism believes education should be used to improve society and address social problems. Experimentalism views life as an experiment and emphasizes learning by doing. Existentialism focuses on individual freedom and responsibility. Perennialism believes in timeless knowledge and values and emphasizes traditional academic subjects.
Functions allow programmers to break programs into smaller, reusable parts. There are two types of functions in C: library functions and user-defined functions. User-defined functions make programs easier to understand, debug, test and maintain. Functions are declared with a return type and can accept arguments. Functions can call other functions, allowing for modular and structured program design.
The document discusses making inferences by drawing conclusions based on evidence and reasoning. It provides examples of inferences about characters' personalities, themes of stories, and symbols. Readers can infer character development, themes, symbols, and plot details by analyzing clues in the text and using their own thinking. Making inferences involves reading between the lines and using evidence from what is observed or read to draw conclusions. Activities are suggested for students to practice making inferences based on images, short films, and notes.
The document discusses the history and evolution of smart home technology from the 1970s to present day. It provides examples of early smart home systems like the X10 protocol and highlights Bill Gates' advanced smart home system. The main body explains Internet of Things (IoT) and how it is being applied to smart homes through interconnected devices that enable automation and remote monitoring of home appliances, security systems, and more. Examples of IoT applications in home automation are described, including remote temperature control, lighting, and integrated surveillance systems that provide notifications.
The document discusses network redundancy and spanning tree protocols. It explains that redundant links between devices provide backup paths in case of failure, but can also cause loops. Spanning tree protocols select the best path and block redundant paths to prevent loops. They dynamically unblock backup paths if the primary path fails to maintain connectivity while avoiding loops.
This document provides an overview and introduction to the course "Knowledge Representation & Reasoning" taught by Ms. Jawairya Bukhari. It discusses the aims of developing skills in knowledge representation and reasoning using different representation methods. It outlines prerequisites like artificial intelligence, logic, and programming. Key topics covered include symbolic and non-symbolic knowledge representation methods, types of knowledge, languages for knowledge representation like propositional logic, and what knowledge representation encompasses.
This document provides an overview of first-order logic in artificial intelligence:
- First-order logic extends propositional logic by adding objects, relations, and functions to represent knowledge. Objects can include people and numbers, while relations include concepts like "brother of" and functions like "father of".
- A sentence in first-order logic contains a predicate and a subject, represented by a variable. For example, "tall(John)" asserts that John is tall. Quantifiers like "forall" and "exists" are used to structure sentences.
- First-order logic contains constants, variables, predicates, functions, connectives, equality, and quantifiers as its basic elements.
The document provides an overview of propositional logic including:
1. It defines statements, logical connectives, and truth tables. Logical connectives like negation, conjunction, disjunction and others are explained.
2. It discusses various logical concepts like tautology, contradiction, contingency, logical equivalence, and logical implications.
3. It outlines propositional logic rules and properties including commutative, associative, distributive, De Morgan's laws, identity law, idempotent law, and transitive rule.
4. It provides an example of using truth tables to test the validity of an argument about bachelors dying young.
The document provides an overview of Truth Maintenance Systems (TMS) in artificial intelligence. It discusses key aspects of TMS including:
1. Enforcing logical relations among beliefs by maintaining and updating relations when assumptions change.
2. Generating explanations for conclusions by using cached inferences to avoid re-deriving inferences.
3. Finding solutions to search problems by representing problems as sets of variables, domains, and constraints.
The document also covers justification-based and assumption-based TMS, and how a TMS interacts with a problem solver to add and retract assumptions, detect contradictions, and perform belief revision.
This document provides an overview of predicate logic and various techniques for representing knowledge and drawing inferences using predicate logic, including:
- Representing facts as logical statements using predicates, variables, and quantifiers.
- Distinguishing between propositional logic and predicate logic and their abilities to represent objects and relationships.
- Techniques like resolution and Skolem functions that allow inferring new statements from existing ones in a logical and systematic way.
- How computable functions and predicates allow representing relationships that have infinitely many instances, like greater-than, in a computable way.
The document discusses these topics at a high-level and provides examples to illustrate key concepts in predicate logic and automated reasoning.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing actions when their conditions are met. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
The document provides an overview of knowledge representation techniques. It discusses propositional logic, including syntax, semantics, and inference rules. Propositional logic uses atomic statements that can be true or false, connected with operators like AND and OR. Well-formed formulas and normal forms are explained. Forward and backward chaining for rule-based reasoning are summarized. Examples are provided to illustrate various concepts.
This document provides an introduction to First Order Predicate Logic (FOPL). It defines FOPL as symbolized reasoning where sentences are broken down into subjects and predicates. FOPL is more expressive than propositional logic and allows representing almost any English sentence. It discusses features of FOPL such as generalization of propositional logic and more powerful representation. Applications of FOPL include presenting arguments, determining validity, and formulating theories. The document also defines key terms in FOPL such as constants, variables, functions, and predicates.
Propositional logic deals with propositions as units and the connectives that relate them. It has a syntax that defines allowable sentences using proposition symbols and logical connectives like conjunction, disjunction, implication and equivalence. Sentences are formed using Backus Naur Form grammar. Semantics specify how to compute the truth of sentences using truth tables and models. Knowledge bases can be represented as a set of sentences and inference is used to decide if conclusions are true in all models where the KB is true, such as using a truth table algorithm.
The document discusses knowledge representation using propositional logic and predicate logic. It begins by explaining the syntax and semantics of propositional logic for representing problems as logical theorems to prove. Predicate logic is then introduced as being more versatile than propositional logic for representing knowledge, as it allows quantifiers and relations between objects. Examples are provided to demonstrate how predicate logic can formally represent statements involving universal and existential quantification.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Semantic nets were originally proposed in the 1960s as a way to represent the meaning of English words using nodes, links, and link labels. Nodes represent concepts, objects, or situations, links express relationships between nodes, and link labels specify particular relations. Semantic nets can represent data through examples, perform intersection searches to find relationships between objects, partition networks to distinguish individual from general statements, and represent non-binary predicates. While semantic nets provide a visual way to organize knowledge, they can have issues with inheritance and placing facts appropriately.
Non-monotonic reasoning allows conclusions to be retracted when new information is introduced. It is used to model plausible reasoning where defaults may be overridden. For example, it is typically true that birds fly, so we could conclude that Tweety flies since Tweety is a bird. However, if we are later told Tweety is a penguin, we would retract the conclusion that Tweety flies since penguins do not fly despite being birds. Non-monotonic reasoning resolves inconsistencies by removing conclusions derived from default rules when specific countervailing information is received.
This document discusses propositional logic and knowledge representation. It introduces propositional logic as the simplest form of logic that uses symbols to represent facts that can then be joined by logical connectives like AND and OR. Truth tables are presented as a way to determine the truth value of propositions connected by these logical operators. The document also discusses concepts like models of formulas, satisfiable and valid formulas, and rules of inference like modus ponens and disjunctive syllogism that allow deducing new facts from initial propositions. Examples are provided to illustrate each concept.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It describes representing knowledge at two levels: the knowledge level, which describes facts, and the symbol level, where facts are represented using symbols that can be manipulated by programs. Different knowledge representation schemes are examined, including databases, semantic networks, logic, procedural representations, and choosing an appropriate level of granularity. Issues around representing sets of objects and selecting the right knowledge structure are also covered.
Fuzzy relations, fuzzy graphs, and the extension principle are three important concepts in fuzzy logic. Fuzzy relations generalize classical relations to allow partial membership and describe relationships between objects to varying degrees. Fuzzy graphs describe functional mappings between input and output linguistic variables. The extension principle provides a procedure to extend functions defined on crisp domains to fuzzy domains by mapping fuzzy sets through functions. These concepts form the foundation of fuzzy rules and fuzzy arithmetic.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
The document discusses inference in first-order logic. It provides a brief history of reasoning and logic. It then discusses reducing first-order inference to propositional inference using techniques like universal instantiation and existential instantiation. It introduces the concepts of unification and generalized modus ponens to perform inference in first-order logic. Forward chaining and resolution are also discussed as algorithms for performing inference in first-order logic.
- The document discusses the mathematical foundations of computer science, including topics like mathematical logic, set theory, algebraic structures, and graph theory.
- It specifically focuses on mathematical logic, defining statements, atomic and compound statements, and various logical connectives like negation, conjunction, disjunction, implication, biconditional, and their truth tables.
- It also discusses logical concepts like tautologies, contradictions, contingencies, logical equivalence, and tautological implication through the use of truth tables and logical formulas.
This document discusses truth tables and logical equivalences in propositional logic. It defines truth tables for various logical connectives like negation, conjunction, and disjunction. It also defines logical concepts like tautology, contradiction, and De Morgan's laws. Examples are provided to illustrate double negation, associative laws, distributive laws, and how to determine if a statement is a tautology or contradiction using truth tables.
This document provides an overview and introduction to the course "Knowledge Representation & Reasoning" taught by Ms. Jawairya Bukhari. It discusses the aims of developing skills in knowledge representation and reasoning using different representation methods. It outlines prerequisites like artificial intelligence, logic, and programming. Key topics covered include symbolic and non-symbolic knowledge representation methods, types of knowledge, languages for knowledge representation like propositional logic, and what knowledge representation encompasses.
This document provides an overview of first-order logic in artificial intelligence:
- First-order logic extends propositional logic by adding objects, relations, and functions to represent knowledge. Objects can include people and numbers, while relations include concepts like "brother of" and functions like "father of".
- A sentence in first-order logic contains a predicate and a subject, represented by a variable. For example, "tall(John)" asserts that John is tall. Quantifiers like "forall" and "exists" are used to structure sentences.
- First-order logic contains constants, variables, predicates, functions, connectives, equality, and quantifiers as its basic elements.
The document provides an overview of propositional logic including:
1. It defines statements, logical connectives, and truth tables. Logical connectives like negation, conjunction, disjunction and others are explained.
2. It discusses various logical concepts like tautology, contradiction, contingency, logical equivalence, and logical implications.
3. It outlines propositional logic rules and properties including commutative, associative, distributive, De Morgan's laws, identity law, idempotent law, and transitive rule.
4. It provides an example of using truth tables to test the validity of an argument about bachelors dying young.
The document provides an overview of Truth Maintenance Systems (TMS) in artificial intelligence. It discusses key aspects of TMS including:
1. Enforcing logical relations among beliefs by maintaining and updating relations when assumptions change.
2. Generating explanations for conclusions by using cached inferences to avoid re-deriving inferences.
3. Finding solutions to search problems by representing problems as sets of variables, domains, and constraints.
The document also covers justification-based and assumption-based TMS, and how a TMS interacts with a problem solver to add and retract assumptions, detect contradictions, and perform belief revision.
This document provides an overview of predicate logic and various techniques for representing knowledge and drawing inferences using predicate logic, including:
- Representing facts as logical statements using predicates, variables, and quantifiers.
- Distinguishing between propositional logic and predicate logic and their abilities to represent objects and relationships.
- Techniques like resolution and Skolem functions that allow inferring new statements from existing ones in a logical and systematic way.
- How computable functions and predicates allow representing relationships that have infinitely many instances, like greater-than, in a computable way.
The document discusses these topics at a high-level and provides examples to illustrate key concepts in predicate logic and automated reasoning.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing actions when their conditions are met. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
The document provides an overview of knowledge representation techniques. It discusses propositional logic, including syntax, semantics, and inference rules. Propositional logic uses atomic statements that can be true or false, connected with operators like AND and OR. Well-formed formulas and normal forms are explained. Forward and backward chaining for rule-based reasoning are summarized. Examples are provided to illustrate various concepts.
This document provides an introduction to First Order Predicate Logic (FOPL). It defines FOPL as symbolized reasoning where sentences are broken down into subjects and predicates. FOPL is more expressive than propositional logic and allows representing almost any English sentence. It discusses features of FOPL such as generalization of propositional logic and more powerful representation. Applications of FOPL include presenting arguments, determining validity, and formulating theories. The document also defines key terms in FOPL such as constants, variables, functions, and predicates.
Propositional logic deals with propositions as units and the connectives that relate them. It has a syntax that defines allowable sentences using proposition symbols and logical connectives like conjunction, disjunction, implication and equivalence. Sentences are formed using Backus Naur Form grammar. Semantics specify how to compute the truth of sentences using truth tables and models. Knowledge bases can be represented as a set of sentences and inference is used to decide if conclusions are true in all models where the KB is true, such as using a truth table algorithm.
The document discusses knowledge representation using propositional logic and predicate logic. It begins by explaining the syntax and semantics of propositional logic for representing problems as logical theorems to prove. Predicate logic is then introduced as being more versatile than propositional logic for representing knowledge, as it allows quantifiers and relations between objects. Examples are provided to demonstrate how predicate logic can formally represent statements involving universal and existential quantification.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Semantic nets were originally proposed in the 1960s as a way to represent the meaning of English words using nodes, links, and link labels. Nodes represent concepts, objects, or situations, links express relationships between nodes, and link labels specify particular relations. Semantic nets can represent data through examples, perform intersection searches to find relationships between objects, partition networks to distinguish individual from general statements, and represent non-binary predicates. While semantic nets provide a visual way to organize knowledge, they can have issues with inheritance and placing facts appropriately.
Non-monotonic reasoning allows conclusions to be retracted when new information is introduced. It is used to model plausible reasoning where defaults may be overridden. For example, it is typically true that birds fly, so we could conclude that Tweety flies since Tweety is a bird. However, if we are later told Tweety is a penguin, we would retract the conclusion that Tweety flies since penguins do not fly despite being birds. Non-monotonic reasoning resolves inconsistencies by removing conclusions derived from default rules when specific countervailing information is received.
This document discusses propositional logic and knowledge representation. It introduces propositional logic as the simplest form of logic that uses symbols to represent facts that can then be joined by logical connectives like AND and OR. Truth tables are presented as a way to determine the truth value of propositions connected by these logical operators. The document also discusses concepts like models of formulas, satisfiable and valid formulas, and rules of inference like modus ponens and disjunctive syllogism that allow deducing new facts from initial propositions. Examples are provided to illustrate each concept.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It describes representing knowledge at two levels: the knowledge level, which describes facts, and the symbol level, where facts are represented using symbols that can be manipulated by programs. Different knowledge representation schemes are examined, including databases, semantic networks, logic, procedural representations, and choosing an appropriate level of granularity. Issues around representing sets of objects and selecting the right knowledge structure are also covered.
Fuzzy relations, fuzzy graphs, and the extension principle are three important concepts in fuzzy logic. Fuzzy relations generalize classical relations to allow partial membership and describe relationships between objects to varying degrees. Fuzzy graphs describe functional mappings between input and output linguistic variables. The extension principle provides a procedure to extend functions defined on crisp domains to fuzzy domains by mapping fuzzy sets through functions. These concepts form the foundation of fuzzy rules and fuzzy arithmetic.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
The document discusses inference in first-order logic. It provides a brief history of reasoning and logic. It then discusses reducing first-order inference to propositional inference using techniques like universal instantiation and existential instantiation. It introduces the concepts of unification and generalized modus ponens to perform inference in first-order logic. Forward chaining and resolution are also discussed as algorithms for performing inference in first-order logic.
- The document discusses the mathematical foundations of computer science, including topics like mathematical logic, set theory, algebraic structures, and graph theory.
- It specifically focuses on mathematical logic, defining statements, atomic and compound statements, and various logical connectives like negation, conjunction, disjunction, implication, biconditional, and their truth tables.
- It also discusses logical concepts like tautologies, contradictions, contingencies, logical equivalence, and tautological implication through the use of truth tables and logical formulas.
This document discusses truth tables and logical equivalences in propositional logic. It defines truth tables for various logical connectives like negation, conjunction, and disjunction. It also defines logical concepts like tautology, contradiction, and De Morgan's laws. Examples are provided to illustrate double negation, associative laws, distributive laws, and how to determine if a statement is a tautology or contradiction using truth tables.
This document provides an introduction to First Order Predicate Logic (FOPL). It discusses the differences between propositional logic and FOPL, the parts and syntax of FOPL including terms, atomic sentences, quantifiers and rules of inference. The semantics of FOPL are also explained. Pros and cons are provided, such as FOPL's ability to represent individual entities and generalizations compared to propositional logic. Applications include using FOPL as a framework for formulating theories.
The document discusses propositional logic including:
- Propositional logic uses propositions that can be either true or false and logical connectives to connect propositions.
- It introduces syntax of propositional logic including atomic and compound propositions.
- Logical connectives like negation, conjunction, disjunction, implication, and biconditional are explained along with their truth tables and significance.
- Other concepts discussed include precedence of connectives, logical equivalence, properties of operators, and limitations of propositional logic.
- Examples are provided to illustrate propositional logic concepts like truth tables, logical equivalence, and translating English statements to symbolic form.
Propositional logic is presented. A proposition is a statement that can be either true or false. Logical connectives like negation, conjunction, disjunction, conditional, biconditional, NOR, NAND and XOR are used to combine propositions. A tautology is a proposition that is always true, while a contradiction is always false. Truth tables are used to determine if a proposition is a tautology, contradiction or contingency. Logical equivalence means that two propositions have the same truth values according to their truth tables.
This document summarizes key concepts in logic:
1. Statements are declarative sentences that can be true or false. Logical connectives like "and", "or", and "not" are used to combine statements.
2. A tautology is a proposition that is always true, while a contradiction is always false. A contingency can be either true or false.
3. Logical equivalence means two statements always have the same truth value. Direct, converse, inverse, and contrapositive are types of logical implications between statements.
This document summarizes key concepts in logic:
1. Statements are declarative sentences that can be true or false. Logical connectives like "and", "or", and "not" are used to combine statements.
2. A tautology is a proposition that is always true, while a contradiction is always false. A contingency can be either true or false.
3. Logical equivalence means two statements always have the same truth value. Direct, converse, inverse, and contrapositive are types of logical implications between statements.
This document discusses properties that a good domain description for reasoning about actions should have beyond mere consistency. It introduces the concept of modularity for action theories, where the different types of laws (static, effect, executability, inexecutability) are arranged in separate components with limited interaction. Violations of the proposed postulates about modularity can lead to unexpected conclusions from logically consistent theories. The document outlines algorithms to check whether an action theory satisfies the postulates of modularity.
A proposition is a statement that can be either true or false. Connectives like AND, OR, and IF-THEN are used to combine propositions. Truth tables are mathematical tables that define the logical relationships between simple or compound propositions by systematically assigning true and false values and determining the resulting truth values.
This document introduces predicate logic, including predicate symbols and signatures, logical connectives like negation and conjunction, quantifiers like universal and existential, the syntax and semantics of predicate logic formulas, and some useful equivalences. Predicate logic allows representing relations between objects using predicates of varying arities and expressing properties of collections using quantifiers. Formulas in predicate logic can be built from predicate and function symbols, terms, quantifiers, and logical connectives.
Logic is used to reason about the truth or falsity of statements. Propositional logic deals with Boolean functions while predicate logic deals with quantified Boolean functions. Statements can be combined using logical connectives like AND, OR, IMPLIES. Their truth values are determined using truth tables. Logical statements can be translated between English and symbolic notation. Predicate logic involves functions whose values depend on variables that range over a domain. Quantifiers like "for all" and "for some" are used to make assertions about predicates over a domain.
The document discusses how truth tables can be used to determine the logical status of propositions and arguments. Truth tables assign truth values (True/False) to propositions based on the truth values of their component statements, allowing the logical status of single propositions and groups of propositions to be determined. The logical status can be tautology, contradiction, contingent, equivalent, satisfiable/consistent, or unsatisfiable/inconsistent depending on the truth values. Validity of arguments can also be determined from truth tables by checking if the conclusion is true in all rows where the premises are true. Examples of truth tables are provided to illustrate these concepts.
This document discusses type-dependent name resolution in programming languages. It notes that sometimes type information is needed before name resolution can be performed, such as when resolving names in records where the record fields depend on the types. It gives an example where a program defines two records A and B, with B containing a field of type A, and names must be resolved through the record types. The document suggests that name resolution and type checking/inference can often be done in either order for languages, but type information is sometimes necessary for resolving some names.
This document discusses logic-based knowledge representation using propositional and predicate logic. It covers the syntax, semantics, and key concepts of both logics. For propositional logic, it defines propositional symbols, logical connectives, truth tables, and valid/satisfiable sentences. For predicate logic, it introduces predicates, variables, quantifiers, and how to form atomic and complex sentences using terms, predicates, and logical connectives. Variable quantifiers like universal and existential are also explained with examples.
The document discusses compound propositions and logical expressions. It defines atomic and compound expressions, and explains that compound expressions contain logical connectives. Precedence rules for logical operators are provided. Truth tables are used to evaluate expressions and determine if they are tautologies or contradictions. Examples demonstrate logical equivalences using truth tables, such as De Morgan's laws, distributivity, and contrapositives.
The document discusses truth tables and logical connectives such as conjunction, disjunction, negation, implication and biconditionals. It provides examples of truth tables for compound propositions involving multiple variables. De Morgan's laws are explained, which state that the negation of a conjunction is the disjunction of the negations, and the negation of a disjunction is the conjunction of the negations. The concepts of tautologies, contradictions and logical equivalence are also covered.
The document discusses the 2009 hacking of emails from the Climatic Research Unit at the University of East Anglia which led to public questions about the reliability and honesty of climate scientists. It summarizes concerns raised about the scientists' expressed reluctance to share data and code as requested, as well as references to a "trick" used to "hide the decline". However, it also notes that nothing in the emails provides evidence of a conspiracy or data falsification. The document examines issues around scientific transparency, biases, and building public trust in science.
This document summarizes Thomas Kuhn's view of scientific progress and theory change, and responses to Kuhn from philosophers Lakatos, Laudan, and Feyerabend. Kuhn argued that science progresses through "paradigm shifts" rather than continuous progress, observations are theory-laden, and the choice between paradigms is not fully objective. Later philosophers criticized aspects of Kuhn's view and proposed alternative models of scientific progress and rational theory evaluation.
This document discusses the philosophical ideas of Duhem, Quine, and challenges in theory testing and scientific methodology. It addresses the concepts of underdetermination of theory by data, holism in testing, and meaning holism. It explains that scientific theories are tested as groups, not in isolation, and that changing one hypothesis can require changes throughout the theoretical framework. The document uses examples like Newton's laws of motion and predictions of planetary orbits to illustrate these concepts.
The document discusses Semmelweis' work in the 1840s to address childbed fever in Vienna hospitals. Semmelweis observed that the mortality rate from childbed fever was much higher in the first division ward staffed by doctors and medical students compared to the second division ward staffed by midwives. Through a process of elimination, Semmelweis hypothesized that cadaveric particles carried from autopsies to patients caused the disease. Reducing these particles by washing hands and instruments with chlorinated lime lowered the mortality rate, supporting his hypothesis. However, his conclusion could not be logically proven through the evidence, as other factors could also explain the outcome. The document also discusses the problem of induction in drawing conclusions about
The document discusses the history of human subjects research, including unethical experiments conducted by Nazi Germany scientists and the Tuskegee Syphilis Experiment conducted by the U.S. Public Health Service from 1932 to 1972. The Nazi experiments violated subjects' rights and caused extreme harm. The Tuskegee study intended to observe untreated syphilis in black men but became unscientific when subjects received some treatment and contributed no useful medical knowledge. It was later deemed unethical by modern standards for failing to obtain informed consent and exposing subjects to harm without benefit.
The document discusses the historical evolution of ethics guidelines for research involving human subjects. It begins with the Hippocratic Oath from ancient Greece which established early standards of medical ethics. It then discusses the Nuremberg Code created after World War II which focused on informed consent and avoiding harming subjects. Finally, it examines the Declaration of Helsinki which recognized the need for institutional review and emphasized informed consent, assessing risks/benefits, and protecting subject well-being.
This document discusses several ethical issues regarding human subjects research, particularly in developing countries. It summarizes the Belmont Report's principles of respect for persons, beneficence, and justice. It then examines debates around the ethics of placebo-controlled trials in developing world contexts and whether local standards of care are acceptable for control groups. The tension between beneficence toward subjects and producing scientifically valid results that could benefit the population is also discussed.
This document discusses scientific communication and the process of publishing scientific papers. It outlines the typical sections of a scientific paper, including the introduction, methods, results, and discussion. It also describes the role of peer review in evaluating papers before publication. While scientific papers aim to contribute to shared knowledge, they also serve as a way for scientists to establish priority and build their career records. This can complicate communication if it discourages sharing negative results or stretching the interpretation of findings. The document examines criticisms of the standard scientific paper format and considers other modes of scientific communication.
This document discusses authorship standards and issues in scientific publishing. It outlines why author order and attribution matter for communicating who did the research and granting proper credit. It describes problematic authorship situations like ghost writers and guest authors that can mislead readers. The document also summarizes International Committee of Medical Journal Editors standards for authorship and calls for more explicit identification of author contributions to increase accountability. It raises some concerns about peer review being misused to delay competitors' work.
This document discusses several issues relating to patents and intellectual property:
- There is a tension between the norm of open scientific knowledge and the concept of intellectual property ownership.
- Employers and universities often claim ownership over intellectual property created by employees and students. Patent law varies by jurisdiction on this issue.
- Patents provide a limited monopoly on inventions in exchange for publicly disclosing details that allow others to replicate the work once the patent expires. However, critics argue the patent system does not always encourage innovation or serve the public interest.
Lec16 International Strategies for Scientific DialogueJanet Stemwedel
International strategies for scientific dialogue discusses various cultural influences on scientific communities and how scientists navigate these differences. Some key points summarized:
1. Local cultures and institutions can influence how science is practiced differently than an idealized meritocratic model. Hierarchies form within communities based on factors like field of study or country of origin.
2. Japanese scientists aim to participate globally while maintaining their national identity, but risk being seen as "strange" if habits from abroad are acknowledged.
3. The Tsukuba Science City project departed from traditional Japanese models by focusing on a less prestigious field outside standard university channels. This lowered their status nationally but raised it internationally in that field.
The document discusses different types of scientific explanations and criteria for a good explanation. It presents the deductive-nomological model of explanation but notes some limitations, as not all good explanations fit this model and some arguments that fit the model may not be good explanations. Different examples are provided to illustrate explanations based on laws of nature, statistical generalizations, mechanisms, and pragmatic or contextual factors.
The document discusses various positions regarding scientific realism and antirealism. It presents an example from the 19th century of a scientist, Jones, developing a theory that an unobservable microbe called a "crobe" is responsible for transmitting disease in the same way that lice transmit diseases. While the theory is empirically successful, some argue we cannot know if crobes truly exist. The document explores arguments for scientific realism, antirealism, and a middle position of "entity realism," discussing what types of claims we can and cannot make regarding observable versus unobservable entities based on empirical evidence and scientific theories.
The document discusses several topics relating to naturalism as a philosophical approach:
1) Naturalism shifts the question from how to justify scientific methodology to how to adequately describe how knowledge and science work based on what science tells us.
2) A naturalist view is that science can describe the belief-forming mechanisms that humans use and philosophy can evaluate how good those mechanisms are for achieving different goals.
3) Several philosophers discussed apply scientific findings and methods to philosophical problems, including describing how social and reward structures within science help explain its success.
The document discusses feminist critiques of science, specifically examining how the exclusion of women from science may have been harmful. It explores how scientific theories in the past have promoted gender biases and stereotypes to justify excluding women. A key feminist critique is that excluding diverse perspectives, like those of women, reduces objectivity by limiting alternative viewpoints and questions that could be asked. The document argues that including more women in science can help address implicit biases and assumptions, leading to more rigorous and objective scientific research.
The document discusses indirect truth table analysis for determining the validity of arguments. It provides examples of setting up truth tables to evaluate arguments by making the conclusion false and premises true without contradictions. The examples show both valid and invalid arguments based on whether a consistent truth assignment can be found.
The document discusses translating statements from English to propositional logic, including:
- Conjunction and disjunction are commutative but order matters for statements with mixed operators
- How to translate conditional statements like "if P then Q" and biconditionals like "P if and only if Q"
- Necessary and sufficient conditions and how they relate to conditionals
- Examples of translating various English language statements into propositional logic statements
The document discusses Thomas Kuhn's concept of scientific revolutions and paradigm shifts. It explains that normal science operates within a shared paradigm, but anomalies can lead to a crisis when the paradigm is unable to solve resistant puzzles. This forces scientists to consider alternatives. When comparing paradigms like Ptolemaic vs Copernican models of planetary motion, factors like how well each addresses unsolved puzzles, fits with other theories and observations, and aesthetic appeal influence which paradigm gains acceptance. However, paradigm choices are subjective since what we observe depends on the paradigm.
The document discusses translating natural language statements into propositional logic by identifying logical structures like negation, conjunction, disjunction, etc. It provides examples of translating statements involving negation (e.g. "Bill does not own a car"), conjunction (e.g. "Jenny went to the park and Bill went to the park"), disjunction (e.g. "Either my roommate will bring the textbook or my lab partner will let me borrow hers"), and discusses how to properly capture meaning and logical relationships. Key concepts covered are using variables to represent propositions, appropriate use of logical operators, and handling collective subjects, temporal sequences, and additive comparisons.
Thomas Kuhn argues that science operates in two distinct modes: normal science and scientific revolution. During normal science, scientists work within a shared paradigm that provides the framework and assumptions for their research. The paradigm guides what phenomena can be explained, what problems are worth studying, and how research is conducted. However, over time anomalies and resistant problems emerge that the paradigm cannot resolve, leading to a crisis and eventual shift to a new paradigm during a period of scientific revolution.
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How to Create Kanban View in Odoo 18 - Odoo SlidesCeline George
The Kanban view in Odoo is a visual interface that organizes records into cards across columns, representing different stages of a process. It is used to manage tasks, workflows, or any categorized data, allowing users to easily track progress by moving cards between stages.
How to Share Accounts Between Companies in Odoo 18Celine George
In this slide we’ll discuss on how to share Accounts between companies in odoo 18. Sharing accounts between companies in Odoo is a feature that can be beneficial in certain scenarios, particularly when dealing with Consolidated Financial Reporting, Shared Services, Intercompany Transactions etc.
The role of wall art in interior designingmeghaark2110
Wall patterns are designs or motifs applied directly to the wall using paint, wallpaper, or decals. These patterns can be geometric, floral, abstract, or textured, and they add depth, rhythm, and visual interest to a space.
Wall art and wall patterns are not merely decorative elements, but powerful tools in shaping the identity, mood, and functionality of interior spaces. They serve as visual expressions of personality, culture, and creativity, transforming blank and lifeless walls into vibrant storytelling surfaces. Wall art, whether abstract, realistic, or symbolic, adds emotional depth and aesthetic richness to a room, while wall patterns contribute to structure, rhythm, and continuity in design. Together, they enhance the visual experience, making spaces feel more complete, welcoming, and engaging. In modern interior design, the thoughtful integration of wall art and patterns plays a crucial role in creating environments that are not only beautiful but also meaningful and memorable. As lifestyles evolve, so too does the art of wall decor—encouraging innovation, sustainability, and personalized expression within our living and working spaces.
Redesigning Education as a Cognitive Ecosystem: Practical Insights into Emerg...Leonel Morgado
Slides used at the Invited Talk at the Harvard - Education University of Hong Kong - Stanford Joint Symposium, "Emerging Technologies and Future Talents", 2025-05-10, Hong Kong, China.
This slide is an exercise for the inquisitive students preparing for the competitive examinations of the undergraduate and postgraduate students. An attempt is being made to present the slide keeping in mind the New Education Policy (NEP). An attempt has been made to give the references of the facts at the end of the slide. If new facts are discovered in the near future, this slide will be revised.
This presentation is related to the brief History of Kashmir (Part-I) with special reference to Karkota Dynasty. In the seventh century a person named Durlabhvardhan founded the Karkot dynasty in Kashmir. He was a functionary of Baladitya, the last king of the Gonanda dynasty. This dynasty ruled Kashmir before the Karkot dynasty. He was a powerful king. Huansang tells us that in his time Taxila, Singhpur, Ursha, Punch and Rajputana were parts of the Kashmir state.
History Of The Monastery Of Mor Gabriel Philoxenos Yuhanon Dolabanifruinkamel7m
History Of The Monastery Of Mor Gabriel Philoxenos Yuhanon Dolabani
History Of The Monastery Of Mor Gabriel Philoxenos Yuhanon Dolabani
History Of The Monastery Of Mor Gabriel Philoxenos Yuhanon Dolabani
How to Clean Your Contacts Using the Deduplication Menu in Odoo 18Celine George
In this slide, we’ll discuss on how to clean your contacts using the Deduplication Menu in Odoo 18. Maintaining a clean and organized contact database is essential for effective business operations.
How to Manage Amounts in Local Currency in Odoo 18 PurchaseCeline George
In this slide, we’ll discuss on how to manage amounts in local currency in Odoo 18 Purchase. Odoo 18 allows us to manage purchase orders and invoices in our local currency.
How to Manage Amounts in Local Currency in Odoo 18 PurchaseCeline George
Syntax and semantics of propositional logic
1. The Syntax and Semantics of Propositional Logic Phil 57 section 3 San Jose State University Fall 2010
2. Valid arguments are truth-preserving If premises are true, conclusion must be true. Validity is a formal property – not a matter of content or context. How to test whether an argument is valid? Propositional Logic (aka Sentential Logic)
3. PL as a formal system to test arguments: Step 1: Identify argument “in the wild” (in a natural language, like English) Step 2: Translate the argument into PL Step 3: Use formal test procedure within PL to determine whether argument is valid Note that good translation is crucial!
4. Important features of PL Symbols (to capture claims and logical connection between claims) Syntax (the rules for how to take generate complex claims from simple ones) Semantics (the meanings of the atomic units, and rules governing how meanings of atomic units are put together to form complex meanings)
5. Syntax of PL Using logical connectives and operators (which connect or operate on propositions) Symbols: Use letters (P, Q, R, … X, Y, Z) to stand for specific statements Unary propositional operator: ~ Binary propositional connectives: , , , Grouping symbols: ( ), [ ]
6. Syntax of PL Negation; not : ~ ~P Conjunction; and : P Q Disjunction; or : P Q Material conditional; if … then .. : P Q Biconditional: … if and only if … : P Q
7. “ Good grammar” in PL: well-formed formula (wff) Every statement letter P, … Z is a well-formed formula (wff) If p and q are wffs, then so are: (i) ~p (ii) (p q) (iii) (p q) (iv) (p q) (v) (p q) (3) Nothing is a wff unless rules (1) and (2) imply that it is.
8. Syntax of PL Strings that are not wffs: (P~Q) ( QP) ( R) Strings that are wffs: ((P Q) R) ~(X (Y Z))
9. Syntax of PL In PL, every compound formula is one of the following: negation conjunction disjunction conditional biconditional To determine which one, isolate main connective or operator.
11. Syntax of PL By convention, we can drop the outermost set of parentheses if the main connective is not unary (~) (P Q) R Y Z X (Y Z) ~(X (Y Z))
12. Syntax of PL Important note: ~(P Q) is not equivalent to ~P Q
13. Semantics of PL Semantic rules of PL tell us how the meaning of its constituent parts, and their mode of combination, determine the meaning of a compound statement. Logical operators in PL determine what the truth-values of compound statements are depending on the truth-values of the formulae in the compound.
14. Semantics of PL Logical operators defined by truth-tables . (T= true, F=false) Negation: P ~P T F F T
15. Semantics of PL Conjunction: P Q P Q T T T T F F F T F F F F
16. Semantics of PL Disjunction: P Q P Q T T T T F T F T T F F F
17. Semantics of PL Material conditional: P Q P Q T T T T F F F T T F F T
18. Semantics of PL Biconditional: P Q P Q T T T T F F F T F F F T