Lecture-10-11-Logical_Agents

Lecture-10-11-Logical_Agents - CS 561: Artificial...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, [email protected] TA: Harris Chiu ( [email protected] ), Wed 2:45-4:45pm, PHE 328 Penny Pan ( [email protected] ), Fri 10am-noon, PHE 328 Lectures: MW 5:00-6:20pm, ZHS 159 Office hours: By appointment Class page: http://www-bcf.usc.edu/~macskass/CS561/Fall2010/ This class will use https://blackboard.usc.edu/webapps/login/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (3rd ed)
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CS561 - Lecture 09-10 - Macskassy - Fall 2010 2 Logical Agents [AIMA Ch. 7] Knowledge-based agents Wumpus world Logic in general models and entailment Propositional (Boolean) logic Equivalence, validity, satisfiability Inference rules and theorem proving forward chaining backward chaining resolution
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CS561 - Lecture 09-10 - Macskassy - Fall 2010 3 Knowledge bases Knowledge base = set of sentences in a formal language Declarative approach to building an agent (or other system): T ELL it what it needs to know Then it can A SK itself what to do answers should follow from the KB Agents can be viewed at the knowledge level i.e., what they know , regardless of how implemented Or at the implementation level i.e., data structures in KB and algorithms that manipulate them
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Knowledge-Based Agent Agent that uses prior or acquired knowledge to achieve its goals Can make more efficient decisions Can make informed decisions Knowledge Base (KB): contains a set of representations of facts about the Agent’s environment Each representation is called a sentence Use some knowledge representation language , to T ELL it what to know e.g., (temperature 72F) A SK agent to query what to do Agent can use inference to deduce new facts from T ELL ed facts Knowledge Base Inference engine Domain independent algorithms Domain specific content TELL ASK 4 CS561 - Lecture 09-10 - Macskassy - Fall 2010
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CS561 - Lecture 09-10 - Macskassy - Fall 2010 5 A simple knowledge-based agent The agent must be able to: Represent states, actions, etc. Incorporate new percepts Update internal representations of the world Deduce hidden properties of the world Deduce appropriate actions
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CS561 - Lecture 09-10 - Macskassy - Fall 2010 6 Wumpus World Example Performance measure gold +1000, death -1000 -1 per step, -10 for using the arrow Environment Squares adjacent to wumpus are smelly Squares adjacent to 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 gold if in same square Releasing drops the gold in same square Actuators Left turn, Right turn, Forward, Grab, Release, Shoot Sensors Breeze, Glitter, Smell
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Observable? Deterministic?
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This note was uploaded on 10/01/2010 for the course CSCI 561 at USC.

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Lecture-10-11-Logical_Agents - CS 561: Artificial...

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