Download Agent-Based Defeasible Control in Dynamic Environments by John-Jules Ch. Meyer, Jan Treur PDF

By John-Jules Ch. Meyer, Jan Treur

This quantity, the seventh quantity within the DRUMS instruction manual sequence, is a part of the aftermath of the profitable ESPRIT undertaking DRUMS (Defeasible Reasoning and Uncertainty administration platforms) which came about in phases from 1989- 1996. within the moment degree (1993-1996) a piece package deal used to be brought dedicated to the subjects Reasoning and Dynamics, overlaying either the subjects of "Dynamics of Reasoning", the place reasoning is considered as a method, and "Reasoning approximately Dynamics", which has to be understood as touching on how either designers of and brokers inside of dynamic structures may possibly cause approximately those structures. the current quantity offers paintings performed during this context prolonged with a few paintings performed through remarkable researchers outdoors the venture on similar concerns. whereas the former quantity during this sequence had its specialise in the dynamics of reasoning seasoned­ cesses, the current quantity is extra desirous about "reasoning approximately dynamics', viz. how (human and synthetic) brokers cause approximately (systems in) dynamic environments so that it will regulate them. particularly we think of modelling frameworks and standard agent types for modelling those dynamic platforms and formal methods to those structures similar to logics for brokers and formal ability to cause approximately agent­ established and compositional platforms, and motion & switch extra regularly. We take this chance to say that we've got very friendly reminiscences of the venture, with its full of life workshops and different conferences, with the numerous websites and researchers concerned, either inside of and outdoors our personal paintings package.

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Extra info for Agent-Based Defeasible Control in Dynamic Environments

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This implies that the component will succeed if all possible targets associated to the task control focus diagnose fault have been derived. The definition of this information is included in the definition of Diagnostic Reasoning's private kernel information as initial kernel information: this specifies that the output atoms the component aims to determine are all atoms of the form diagnosis(H: HYPOTHESIS} for some instantiation of H: HYPOTHESIS for the task control focus diagnose fault. This is specified by the expression: diagnosis(H : HYPOTHESIS} : confirm, where confirm is the target type.

2 Knowledge composition Knowledge composition identifies the knowledge structures at different levels of (knowledge) abstraction, and describes how a knowledge structure can be defined in terms of lower level knowledge structures. 2 shows more than a one to one correspondence between process abstraction levels and knowledge abstraction levels. 1 Identification of knowledge structures at different abstraction levels The two main structures used as building blocks to model knowledge are: information types and knowledge bases.

TREUR Assumption Determination Based on the current state of one diagnostic process, assumptions are generated. Relevant input atoms: Relevant output atoms: rejected(H:HYPOTHESIS, S:SIGN), has_been_considered( H: HYPOTHESIS), observation_result(O:OBSERVATION, S:SIGN) poss_assumption(H:HYPOTHESIS, S:SIGN) Relevant part of the knowledge base: if observation_result( car _starts, neg) and not has_been_considered(battery_empty) then poss_assumption(battery_empty, pos) if rejected(battery_empty, pos) and not has_been_considered(sparking_plugs_problem) then poss_assu mption (sparki ng_pl ugs_problem, pos) if rejected(H:HYPOTHESIS, pos) then poss_assumption(H:HYPOTHESIS, neg) Assumption Evaluation Based on the selected assumption, the predicted and actual observation result, an evaluation is made.

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