AI Chapter 4a PDF

Knowledge and Reasoning Chapter 4  Outline – Knowledge? – Logical Agents (Knowledge Based Agents) – Propositional Logic  Knowledge ??? Data – ??? Information – ??? Knowledge – ??? Wisdom– ???  Knowledge Hierarchy Data – The raw material of inform...

Knowledge and Reasoning Chapter 4  Outline – Knowledge? – Logical Agents (Knowledge Based Agents) – Propositional Logic  Knowledge ??? Data – ??? Information – ??? Knowledge – ??? Wisdom– ???  Knowledge Hierarchy Data – The raw material of information Information – Data organized and presented in a particular manner Knowledge – “Justified true belief” – Information that can be acted upon Wisdom – Distilled and integrated knowledge – Demonstrative of high-level “understanding” Knowledge Hierarchy More refined and abstract Wisdom Knowledge Information Data  Example Data – 98.6º F, 99.5º F, 100.3º F, 101º F, … Information – Hourly body temperature: 98.6º F, 99.5º F, 100.3º F, 101º F, … Knowledge – If you have a temperature above 100º F, you most likely have a fever Wisdom – If you don’t feel well, go see a doctor  Knowledge-Based Agents Hold information about the world in a Knowledge Base (KB) KB is built up of sentences. KB contains background knowledge  Knowledge-Based Agents A knowledge-based agent is an agent that consists of two parts: a knowledge base and an inference engine knowledge base Domain-Specific content Inference engine Domain-independent algorithm  Knowledge-Based Agents The knowledge base contains the domain- specific knowledge that the agent has of its environment. knowledge base can consist of facts, but also rules that describe the structure of the environment. – male(abebe). ………. fact – brotherof(X,Y):-parentof(Z,X),parentof(Z,Y), not(X=Y), male(X). ……… rule  Knowledge-Based Agents The inference engine is domain- independent. It consists of algorithms that take the contents of the knowledge base and infer (i.e. deduce) new knowledge about the world.  Knowledge-Based Agents Knowledge based agents are particularly useful in partially observable environments, If the agent cannot directly perceive parts of the environment it may be able to infer some knowledge about it based on the things that it does know. The knowledge in the knowledge base (KB) must be represented as sentences in some formal logical language. (Ex. Prolog)  Knowledge-Based Agents Whenever the agent receives percepts from the environment they will be converted into sentences and added to the KB. When the inference engine generates new knowledge it is also added to the KB. In addition, when the agent needs to make an action, the agent must ask the KB what the optimal action will be at that time.  Knowledge-Based Agents Therefore a KB must define two operations: – there must be some way of telling the KB a new piece of information; – and there must be some way of asking the KB some question about the environment. male(david). ?- parent(X,Y). female(susan). X = david, parent(david,susan). Y = susan.  Representation, Reasoning and Logic Syntax: Describes the symbols in a language and how they can be used together. Semantics: Gives meaning to the syntax. Defines how the symbols in the syntax relate to in the real world. Entailment: If x entails y, then if x is true y is true.  Logic Basically there are two types of logics. Propositional logic: Also called Boolean Logic. Very simple logic. Not very useful for real situations. And, Or, Implies, Equivalent, and Not are the only connectives. First order logic (FoL): More complex logic. Useful for real world examples.  Propositional Logic Syntax Semantics Inference  Syntax Constant: – True, False Symbols: – P, Q, … Parentheses: – () Logical connectives:  (and),  (or),  (implication),  (equivalence),  (not)  Syntax (cond.) The proposition symbols P1, P2 etc are sentences – If S is a sentence, S is a sentence (negation) – If S1 and S2 are sentences, S1  S2 is a sentence (conjunction) – If S1 and S2 are sentences, S1  S2 is a sentence (disjunction) – If S1 and S2 are sentences, S1  S2 is a sentence (implication) – If S1 and S2 are sentences, S1  S2 is a sentence  Semantics All the connectives are defined in a truth table For example: P Q P Q P Q 0 0 1 1 0 1 1 0 1 0 0 0 1 1 1 1 Truth Table  Sematics S is true iff S is false S1  S2 is true iff S1 is true and S2 is true S1  S2 is true iff S1is true or S2 is true S1  S2 is true iff S1 is false or S2 is true i.e., is false iff S1 is true and S2 is false S1  S2 is true iff S1S2 is true andS2S1 is true  Inference It is a conclusion reached on the basis of evidence and reasoning. Inferences are steps in reasoning, moving from premises to conclusions. Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference.  Inference Valid: A sentence that’s true in all situations. e.g., True, A A, A  A, (A  (A  B))  B Satisfiability: A sentence that is true in at least one situation. Unsatisfiability: A sentence that isn’t satisfiable. e.g., AA