Knowledge Representation and Reasoning ? Representa

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Knowledge Representation and Reasoning?Represent
ação do Conhecimento e Raciocínio Computacional

• José Júlio Alferes

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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

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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

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What is this course about?

• Logic approaches to knowledge representation
• Issues in knowledge representation
• semantics, expressivity, complexity
• Representation formalisms
• Forms of reasoning
• Methodologies
• Applications

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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.

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What prior knowledge?

• Computational Logic
• Introduction to Artificial Intelligence
• Logic Programming

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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.

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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
• ...

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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!

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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

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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

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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!

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In this course (revisited)

• Non-Monotonic Logics
• Languages
• Tools
• Methodologies
• Applications
• Description Logics
• Idem