Transcript jan12
CS 4100 Artificial Intelligence Prof. C. Hafner Class Notes Jan 12, 2012 Types of Agents (cont.) Reflex agent • Behavior depends only on current input (no history or model of the overall environment) • Ex: Vacuum Agent – Percepts: 2-tuple: A or B, Clean or Dirty – Actions: Left, Right, Suck, NoOp Vacuum agent’s behavior • The vacuum agent might follow this simple strategy: – if current location is dirty, clean it (Suck) – If current location is clean move to the other location Knowledge Representation: Two Approaches • Procedural representation – Program’s statements directly encode the knowledge (for example, of the strategy to follow) • Declarative representation – A data structure encodes the knowledge and the program’s statements act like an interpreter Implementing the Vacuum Agent Approach 1 (procedural) if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left Implementing the Vacuum agent Approach 2 (declarative): state perceive() rule rule-match(state, rule base) action RHS(rule) return action Rule Base Per cept Action [A, Clean] Right [A, Dirty] Suck … Simple reflex agents Production Rule Systems • Behavior is expressed as a set of production rules (called “table-driven” by RN) 1. Condition Action 2. Condition Action ... Condition called left-hand-side (LHS) Action called right-hand-side (RHS) In what sense is an agent that uses declarative knowledge more intelligent ?? Production rule systems • Drawbacks: – Huge RULE BASE (time consuming to build by hand) – What if more than one condition is satisfied? – Inflexible (no adaptation or learning) Analyzing Agent Performance • Discussion of “rationality” – Must define a performance measure • How clean (but penalty for extra work?) – Rationality maximizes expected value of the measure – Depends on knowledge of the environment • Can a clean square get dirty again? At what rate? Knowledgeable agents Q: What formal language(s) can we use to represent • • • • Current facts about the state of the world General facts about how the world behaves General facts about the effects of actions that the agent can perform Condition action rules that specify how the agent is to behave A: Formal logic • Syntax and semantics well understood • Computational tractability known for important subsets (Horn clause logic) Introduce Python Assignment 1 http://relationalagents.com/demos/index.html Relational Agent Systems How are you feeling today ?