History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
History of Knowledge Representation (SIKS Course 2010)
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History of Knowledge Representation (SIKS Course 2010)

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The goal of AI research is the simulation and approximation of human intelligence by computers. To a large extent this comes down to the development of computational reasoning services that allow machines to solve problems. Robots are the stereotypical example: imagine what a robot needs to know before it is able to interact with the world the way we do? It needs to have a highly accurate internal representation of reality. It needs to turn perception into action, know how to reach its goals, what objects it can use to its advantage, what kinds of objects exist, etc.
The field of knowledge representation (KR) tries to deal with the problems surrounding the incorporation of some body of knowledge (in whatever form) in a computer system, for the purpose of automated, intelligent reasoning. In this sense, knowledge representation is the basic research topic in AI. Any artificial intelligence is dependent on knowledge, and thus on a representation of that knowledge. The history of knowledge representation has been nothing less than turbulent. The roller coaster of promise of the 50's and 60's, the heated debates of the 70's, the decline and realism of the 80's and the ontology and knowledge management hype of the 90's each left a clear mark on contemporary knowledge representation technology and its application.

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  • Aristotle says that 'on the subject of reasoning' he 'had nothing else on an earlier date to speak of’Logic seems to have emerged from dialectics; the earlier philosophers made frequent use of concepts like reductio ad absurdum in their discussions, but never truly understood the logical implications.Reduction ad absurdum: disprove a proposition by following its implications logically to an absurd consequence.
  • Primary Substance (particulars), Secondary Substance (universals)Position -> SittingState -> Armed, Shod
  • Trunk: variant of porphyry’s treeLeafs on the right: questionsLeafs on the left: keyed to rotating disk for generating answersWas a very friendly guy
  • aggressively greedy, or grasping
  • Computational view, influenced by LullPioneer in Symbolic LogicNumbers: Pythagoras, describe reality with numbers
  • Procreativeorgans: stamen (stamina/stamens) : differentia between genera
  • Formal language : FregeSyllogisms of Aristotle are limited: no variables, functions, quantifiers. No disjunction or conjunction
  • … but that isn’t all
  • Hier zit de heuristic adequacy al in!
  • IPS: processor interacts with environment (effector, receptor), stores information in memoryProcessor consists of elementary information processes (eip) and an interpreter
  • A frame system for the same cube, in different situations
  • Interpretive attachments are procedural!
  • Logical level -> to give formal semantics to semantic networks
  • ×