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Knowledge-Based Agents

A knowledge-based agent represents knowledge explicitly using a knowledge base (KB) and employs a process of inference to derive new knowledge and determine actions. This is the foundation of intelligent reasoning in AI.

Architecture

Knowledge Base (KB)

  • Set of sentences expressing assertions about the world
  • Expressed in a knowledge representation language
  • Sentences may be axioms (given facts) or derived from other sentences

Core Operations

TELL: Add new knowledge to the KB ASK: Query what is known (may involve inference) MAKE-PERCEPT-SENTENCE: Convert percepts to KB sentences MAKE-ACTION-QUERY: Ask what action to perform MAKE-ACTION-SENTENCE: Assert action was executed

Agent Cycle

1. TELL KB: perceived facts
2. ASK KB: what action?  [involves inference]
3. TELL KB: action executed
4. Execute action

Inference Requirement

Answers from ASK must follow from previous TELLs: - Inference must be sound (never derive false conclusions) - Inference should be complete (derive all entailed conclusions) - Must be computationally feasible

Levels of Representation

Knowledge Level

  • "Agent knows that Paris is in France"
  • High-level logical description
  • What the agent knows abstractly

Implementation Level

  • Data structures, algorithms, symbols
  • How the knowledge is stored and manipulated
  • System design details

Properties

Advantages: - Composable: Combine knowledge components flexibly - Task-flexible: Accept new goals without reprogramming - Adaptive: Learn new knowledge and update - Explainable: Can justify reasoning

Challenges: - Knowledge acquisition: How to collect all relevant facts? - Representation bottleneck: Finding efficient representations - Computational complexity: Inference can be expensive - Imperfect/uncertain knowledge: Real world is messy

Representation Languages

References

Russell & Norvig (2010): Chapter 7 - Logical Agents