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![Logic
Entailment in Wumpus
World
Situation after detecting
nothing in [1,1], moving
right, breeze in [2,1]
Consider possible
models for ? assuming
only pits
3 Boolean choices => 8
possible models](https://image.slidesharecdn.com/artificialintelligence-130625043253-phpapp02/85/Artificial-intelligence-Logic-Agents-24-320.jpg)
![Logic
Entailment in Wumpus
World
Situation after detecting
nothing in [1,1], moving
right, breeze in [2,1]
Consider possible
models for ? assuming
only pits
3 Boolean choices => 8
possible models](https://image.slidesharecdn.com/artificialintelligence-130625043253-phpapp02/75/Artificial-intelligence-Logic-Agents-24-2048.jpg)
Uploaded byNuruzzaman Milon
Artificial intelligence- Logic Agents
The document describes logical agents and knowledge representation. It contains the following key points: - Logical agents use knowledge representation and reasoning to solve problems and generate new knowledge. This enables intelligent behavior in partially observable environments. - A knowledge-based agent's central component is its knowledge base, which contains sentences in a formal language that can be queried or added to. - Wumpus World is described as an example environment, where the agent must navigate, avoid dangers, and find gold using limited sensory information and logical reasoning. - Propositional and predicate logic are introduced as knowledge representation languages. Forward and backward chaining are also described as techniques for logical inference.
In this document
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Introduces logical agents and the group members presenting. Outlines the presentation content.
Discusses knowledge-based agents, their functions, representation, and reasoning capabilities.
Explains the Wumpus World scenario, performance measures, environment characteristics, and agent actions.
Covers knowledge bases in logic, entailment, inference, soundness, and completeness concepts.Introduces propositional (Boolean) logic, explaining propositions, basic operations, and logical relationships.
Details reasoning methods with Horn clauses, focusing on forward and backward chaining techniques.
Contrast between forward and backward chaining, discussing their applications, advantages, and efficiency.
Thank you note, concluding the presentation.
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Artificial intelligence- Logic Agents
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- 2. Group Members NuruzzamanMilon (IT 09006) Ali Saheb (IT 09001) Sabbir Hossain (IT 09024)
- 3. Contents Knowledge BasedAgents Wumpus World Logic in General Propositional (Boolean) Logic Reasoning with Horn Clauses Forward Chain Backward Chain
- 4. Logical Agents Humanscan know “things” and “reason” Representation: How are the things stored? Reasoning: How is the knowledge used? To solve a problem… To generate more knowledge… Knowledge and reasoning are important to artificial agents because they enable successful behaviors difficult to achieve otherwise Useful in partially observable environments Can benefit from knowledge in very general forms, combining and recombining information
- 5. Knowledge-Based Agents Centralcomponent of a Knowledge-Based Agent is a Knowledge-Base A set of sentences in a formal language Sentences are expressed using a knowledge representation language Two generic functions: TELL - add new sentences (facts) to the Knowledge Base “Tell it what it needs to know” ASK - query what is known from the Knowledge Base “Ask what to do next”
- 6. Knowledge-Based Agents Theagent must be able to: Represent states and actions Incorporate new percepts Update internal representations of the world Deduce hidden properties of the world Deduce appropriate actions Inference Engine Knowledge-Base Domain- Independent Algorithms Domain- Specific Content
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- 8. Knowledge-Based Agents Declarative You can build a knowledge-based agent simply by “TELLing” it what it needs to know Procedural Encode desired behaviors directly as program code Minimizing the role of explicit representation and reasoning can result in a much more efficient system
- 9. Wumpus World PerformanceMeasure Gold +1000, Death – 1000 Step -1, Use arrow -10 Environment Square adjacent to the Wumpus are smelly Squares adjacent to the pit are breezy Glitter iff gold is in the same square Shooting kills Wumpus if you are facing it Shooting uses up the only arrow Grabbing picks up the gold if in the same square Releasing drops the gold in the same square Actuators Left turn, right turn, forward, grab, release, shoot Sensors Breeze, glitter, and smell
- 10. Wumpus World Characterizationof Wumpus World Observable partial, only local perception Deterministic Yes, outcomes are specified Episodic No, sequential at the level of actions Static Yes, Wumpus and pits do not move Discrete Yes Single Agent Yes
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- 19. Other Sticky Situations Breeze in (1,2) and (2,1) No safe actions Smell in (1,1) Cannot move
- 20. Logic Knowledge basesconsist of sentences in a formal language Syntax Sentences are well formed Semantics The “meaning” of the sentence The truth of each sentence with respect to each possible world (model) Example: x + 2 >= y is a sentence x2 + y > is not a sentence x + 2 >= y is true iff x + 2 is no less than y x + 2 >= y is true in a world where x = 7, y=1 x + 2 >= y is false in world where x = 0, y =6
- 21. Logic Entailment meansthat one thing follows logically from another |= |= iff in every model in which is true, is also true if is true, then must be true the truth of is “contained” in the truth of
- 22. Logic Example: AKnowledge Base containing “Cleveland won” “Dallas won” Entails… “Either Cleveland won or Dallas won” Example: x + y = 4 entails 4 = x + y
- 23. Logic A modelis a formally structured world with respect to which truth can be evaluated M is a model of sentence if is true in m Then KB |= if M(KB) M( ) M( ) x x x x x xx xx xxx xx x x x x x x x xxx xxx x x x x x x x x xxxxxx x x xx xxx x x xx x x x x x xx xxx x x x x x x x xxx x x x xxx xxx x xx xxxx x M(KB) xx x x x x x x x x x x
- 24. Logic Entailment inWumpus World Situation after detecting nothing in [1,1], moving right, breeze in [2,1] Consider possible models for ? assuming only pits 3 Boolean choices => 8 possible models
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- 26. Logic Inference isthe process of deriving a specific sentence from a KB (where the sentence must be entailed by the KB) KB |-i = sentence can be derived from KB by procedure I “KB’s are a haystack” Entailment = needle in haystack Inference = finding it
- 27. Logic Soundness iis sound if… whenever KB |-i is true, KB |= is true Completeness i is complete if whenever KB |= is true, KB |-i is true If KB is true in the real world, then any sentence derived from KB by a sound inference procedure is also true in the real world
- 28. Propositional Logic AKABoolean Logic False and True Proposition symbols P1, P2, etc are sentences NOT: If S1 is a sentence, then ¬S1 is a sentence (negation) AND: If S1, S2 are sentences, then S1 S2 is a sentence (conjunction) OR: If S1, S2 are sentences, then S1 S2 is a sentence (disjunction) IMPLIES: If S1, S2 are sentences, then S1 S2 is a sentence (implication) IFF: If S1, S2 are sentences, then S1 S2 is a sentence (biconditional)
- 29. Propositional Logic P Q¬P P Q P Q P Q P Q False False True False False True True False True True False True True False True False False False True False False True True False True True True True
- 30. Reasoning with HornClauses Forward Chaining For each new piece of data, generate all new facts, until the desired fact is generated Data-directed reasoning Backward Chaining To prove the goal, find a clause that contains the goal as its head, and prove the body recursively (Backtrack when you chose the wrong clause) Goal-directed reasoning
- 31. Forward Chaining AND-ORGraph multiple links joined by an arc indicate conjunction – every link must be proved multiple links without an arc indicate disjunction – any link can be proved
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- 40. Backward Chaining Idea:work backwards from the query q: To prove q by BC, Check if q is known already, or Prove by BC all premises of some rule concluding q Avoid loops Check if new subgoal is already on the goal stack Avoid repeated work: check if new subgoal Has already been proved true, or Has already failed
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- 52. Forward Chaining vs. BackwardChaining Forward Chaining is data driven Automatic, unconscious processing E.g. object recognition, routine decisions May do lots of work that is irrelevant to the goal Backward Chaining is goal driven Appropriate for problem solving E.g. “Where are my keys?”, “How do I start the car?” The complexity of BC can be much less than linear in size of the KB
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