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.
• 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
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
What is this course about?
• Logic approaches to knowledge representation
• Issues in knowledge representation– semantics, expressivity, complexity
• Representation formalisms• Forms of reasoning• Methodologies• Applications
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. PereiraSpringer LNAI, 1996
– Nonmonotonic ReasoningG. AntoniouMIT Press, 1996.
What prior knowledge?
• Computational Logic
• Introduction to Artificial Intelligence
• Logic Programming
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.
Mathematical knowledge vs common sense
• Complete vs incomplete knowledge– x : x N → x R
– go_Work → use_car
• Solid inferences vs default ones– In the face incomplete knowledge
– In emergency situations
– In taxonomies
– In legal reasoning
– ...
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!
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
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
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!
In this course (revisited)
• Non-Monotonic Logics– Languages– Tools– Methodologies– Applications
• Description Logics– Idem…
Top Related