Belief Revision meets Philosophy of Science by Erik J. Olsson, Sebastian Enqvist (eds.)

Logic

By Erik J. Olsson, Sebastian Enqvist (eds.)

Belief revision concept and philosophy of technology either aspire to make clear the dynamics of data – on how our view of the realm alterations (typically) within the gentle of recent proof. but those parts of analysis have lengthy appeared surprisingly indifferent from one another, as witnessed by means of the small variety of cross-references and researchers operating in either domain names. One may well speculate as to what has caused this stunning, and maybe unlucky, scenario. One issue can be that whereas trust revision idea has generally been pursued in a backside- up demeanour, targeting the endeavors of unmarried inquirers, philosophers of technology, encouraged by means of logical empiricism, have tended to be extra attracted to technological know-how as a multi-agent or agent-independent phenomenon.

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And the heuristic (“how to search spaces of possibilities and how to match patterns”), although he left in the epistemological part the question about how the information was represented in the memory of a computer. In fact, this proposal dates back to J. McCarthy and P. Hayes (1969). 12 R. Carnota and R. 2 A Presentation of Belief Revision in AI The same year of the “Presidential Address” by Newell, a paper by Jon Doyle and Philip London “. presents an overview of research in an area loosely called belief revision” (see footnote 3).

92 H. Katsuno and A. Mendelzon (1989). 93 Contrasting with Dalal’s comparison, this review of proposals intended to verify which AGM postulates satisfied each one of the analyzed frameworks. 91 28 R. Carnota and R. Rodríguez would satisfy the AGM postulates. Their main result was a theorem for the characterization of the revision operations that satisfied the six (eight) AGM postulates based on a partial (total) preorder among models which only depend on the KB. Using this idea of characterizing revision operations in terms of orders among models of the old and new information, Katsuno and Mendelzon, analyzed, as was already mentioned, the different proposals for knowledge base updating that had been published in the previous years, showing that all of them could be captured by this semantics, just by considering in each case which is the order among models, of the old base as well as the new one, that underlies each construction.

It followed an intense period of exploration in proof systems based on resolution. 37 A few years after, people started to perceive that this was not so: the engine was not powerful enough in practice, not even to prove theorems that were difficult for the human brain or to solve tasks like planning in robots. A reaction surged with the slogan “uniform procedures do not work”, from which it arose, as a positive result, a whole new generation of programming languages in AI. The negative residue of this reaction was “bad press” for logic as a tool in AI: logic was static, did not admit control mechanisms for inference, the failure of theorem proving using resolution implied the failure of logic in general, etc.

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