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Knowledge Representation for Intelligent Agents: Insights and Frameworks

This document explores the essential components of knowledge representation in intelligent agents, emphasizing the interplay between data, information, knowledge, and wisdom (DIKW hierarchy). It discusses critical concepts such as abstraction, generalization, and the capacity for reasoning. Various perspectives, including those of Rodney Brooks and David Kirsh, are examined to highlight the significance of representation in intelligence development. The text also addresses the structure of knowledge and case relations, vital for creating robust knowledge graphs for problem-solving and reasoning in robotics.

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Knowledge Representation for Intelligent Agents: Insights and Frameworks

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  1. KR & RKnowledge Representationfor Intelligent Agents CIS548 November 8, 2006

  2. KR for IA • Reaction alone insufficient • Street crossing • The feint :: war, sports • Data, information, knowledge, wisdom • D – I – K – W Components • Abstraction • Generalization • Aggregation • Individuation • Structure • Maturation Kutztown University

  3. Framing the Debate • Rodney Brooks • Intelligence Without Representation • Rep gets in way of simple level intel • Rep wrong unit of abstraction for intel • Build capability incrementally • Nothing succeeds like success • David Kirsh • Today the Earwig, Tomorrow Man • Insect ethologists ≠ cognitive scientists • Robotics requires theories of reasoning Kutztown University

  4. Concept Using Creatures{Kirsh} • Capacity to predicate • Judgments of identity • New borns • Dogs • Chimps • Praying mantises • Role of thought in action Kutztown University

  5. Intension/extension • Gottlob Frege • Venus = Venus {identity} • Morning star = evening star {fact} • Are these two statements equivalent? • Sense (Sinn) • Reference (Bedeutung) • Same extension (Venus) • Different intension (described object) Kutztown University

  6. Action & Conceptualization{Kirsh, Frege} • Extension – arm movement • Intension – purpose • Wave hello • Wave goodbye • Swat flies • Differentiation – self-awareness of purpose Kutztown University

  7. Conceptualization{Kirsh} • Ego-centric space • Agent at spatial-temporal origin • Agent-oriented concepts • In front of me; behind me; etc. • Public space • World viewed from nowhere • Generalized object-concepts Kutztown University

  8. Knowledge-Rich Tasks{Kirsh} • Predict behavior of others • Project beyond sensory periphery • Gain objective perspective • Engage in problem solving • Counterfactual reasoning • When I get to Wyoming • Operate stimulus-free To make a prairie it takes a clover and one bee, One clover, and a bee, And revery. The revery alone will do, If bees are few. Kutztown University

  9. Knowledge about Knowledge{McCarthy} • John says, “I don’t know Mike’s phone #.” • Jack says, “Yes, you do. Mike is Sam’s roommate.” • Jack says, “Yes, I do know it; it’s 555-1212.” • Did John know Mike’s phone number? • Define ‘know’ • Define “know that I know” • Define “know that I don’t know” • Define “don’t know whether I know” Kutztown University

  10. Knowledge about Knowledge - II{McCarthy} • Two numbers, m & n, are chosen such that: 2 <= m <= n < = 99. • S is told their sum; P is told their product. • P: "I don't know the numbers." • S: "I knew you didn't know. I don't know either." • P: "Now I know the numbers." • S: "Now I know them too." • What are the numbers? Kutztown University

  11. The Structure of Knowledge • The Case for case • Relations and graphs • Cohesiveness of concepts Kutztown University

  12. The Case for Case • Jen gave Brianna a book. • Verb: give • Subject: Jen • Direct object: book • Indirect object: Brianna • Sentence can be parsed to create “knowledge graph” • Knowledge graph can be used to generate sentence Kutztown University

  13. Relations • Express real world associations among discrete objects • Representable as graphs • Graphs yield variety of structures • Directed, undirected • Connected, not connected • Trees • Planar, non-planar • Graphs permit operations on structures • Traversal • Edge/node addition/deletion Kutztown University

  14. To Come • Reasoning • Structural analysis • State Space Search (previous lectures) • Graph traversal • Node information retrieval • Expert Systems • Backward chaining • Forward chaining • Theorem Proving • Semantic tableaux • Resolution Kutztown University

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