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Knowledge Representation and Reasoning  Representação do Conhecimento e Raciocínio Computacional

Knowledge Representation and Reasoning  Representação do Conhecimento e Raciocínio Computacional. José Júlio Alferes and Carlos Viegas Damásio. What is it ?. What data does an intelligent “agent” deal with? - Not just facts or tuples.

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Knowledge Representation and Reasoning  Representação do Conhecimento e Raciocínio Computacional

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  1. Knowledge Representation and ReasoningRepresentação do Conhecimento e Raciocínio Computacional José Júlio Alferes and Carlos Viegas Damásio

  2. What is it ? • What data does an intelligent “agent” deal with? - Not just facts or tuples. • How does an “agent” knows what surrounds it? What are the rules of the game? • One must represent that “knowledge”. • And what to do afterwards with that knowledge? How to draw conclusions from it? How to reason? • Knowledge Representation and Reasoning  AI Algorithms and Data Structures  Computation

  3. What is it good for ? • Fundamental topic in Artificial Intelligence • Planning • Legal Knowledge • Model-Based Diagnosis • Expert Systems • Semantic Web (http://www.w3.org) • Reasoning on the Web (http://www.rewerse.com) • Ontologies and data-modeling

  4. What is this course about? • Logic approaches to knowledge representation • Issues in knowledge representation • semantics, expressivity, complexity • Representation formalisms • Forms of reasoning • Methodologies • Applications

  5. Bibliography • Will be pointed out as we go along (articles, surveys) in the summaries at the web page • For the first part of the syllabus: • Reasoning with Logic Programming J. J. Alferes and L. M. Pereira Springer LNAI, 1996 • Nonmonotonic Reasoning G. Antoniou MIT Press, 1996.

  6. What prior knowledge? • Computational Logic • Introduction to Artificial Intelligence • Logic Programming

  7. Logic for KRR • Logic is a language conceived for representing knowledge • It was developed for representing mathematical knowledge • What is appropriate for mathematical knowledge might not be so for representing common sense • What is appropriate for mathematical knowledge might be too complex for modeling data.

  8. Mathematical knowledge vs common sense • Complete vs incomplete knowledge • " x : x Î N → x Î R • go_Work → use_car • Solid inferences vs default ones • In the face incomplete knowledge • In emergency situations • In taxonomies • In legal reasoning • ...

  9. Monotonicity of Logic • Classical Logic is monotonic T |= F → T U T’ |= F • This is a basic property which makes sense for mathematical knowledge • But is not desirable for knowledge representation in general!

  10. Non-monotonic logics • Do not obey that property • Appropriate for Common Sense Knowledge • Default Logic • Introduces default rules • Autoepistemic Logic • Introduces (modal) operators which speak about knowledge and beliefs • Logic Programming

  11. Logics for Modeling • Mathematical 1st order logics can be used for modeling data and concepts. E.g. • Define ontologies • Define (ER) models for databases • Here monotonicity is not a problem • Knowledge is (assumed) complete • But undecidability, complexity, and even notation might be a problem

  12. Description Logics • Can be seen as subsets of 1st order logics • Less expressive • Enough (and tailored for) describing concepts/ontologies • Decidable inference procedures • (arguably) more convenient notation • Quite useful in data modeling • New applications to Semantic Web • Languages for the Semantic Web are in fact Description Logics!

  13. In this course (revisited) • Non-Monotonic Logics • Languages • Tools • Methodologies • Applications • Description Logics • Idem…

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