Constraining model-based reasoning using contexts
ABSTRACT Web-based customer service has become a norm of business practice with increasing emphasis on modeling customer needs and providing them with targeted or personalized service solutions in a timely fashion. Almost all the commercial Web service systems adopt some kind of simple customer segmentation models and shallow pattern matching or rule-based techniques for high performance. The models built based on these techniques though very efficient have a fundamental limitation in their ability to capture and explain the reasoning in the process of determining and selecting appropriate services or products. However, using deep models (e.g. semantic networks), though desirable for their expressive power, may require significantly more computational resources (e.g. time) for reasoning. This can compromise the system performance. We report on a new approach that represents and uses contextual information in semantic net-based models to constrain and prune potentially very large search space, which results in much improved performance in terms of speed and selectivity as evidenced by the evaluation results.
Article: Notes on formalizing contexts[show abstract] [hide abstract]
ABSTRACT: Abstract These notes discuss formalizing contexts as rst,class objects. The basic relation is ist(c; p). It asserts that the proposition p is true in the context c. The most important formulas relate the propositions true in dieren t contexts. Introducing contexts as formal objects will permit axiomatizations in limited contexts to be expanded to transcend the original limitations. This seems necessary to provide AI programs using logic with certain capabilities that human,fact representation and human,reasoning possess. Fully implementing transcendence seems to require further extensions to mathematical logic, i.e. beyond the nonmonotonic inference methods rst,invented in AI and now studied as a new domain,of logic. Various notations are considered, but these notes are tentative in not proposing a single language with all the desired capabilities.01/1986;
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ABSTRACT: This paper presents a new approach to the knowledge-based composition of processes for image interpretation and analysis. Its computer implementation in the VISIPLAN (Vision Planner) system provides a way of modeling the composition of image analysis processes within a unified, object-centered hierarchical planning framework. The approach has been tested and shown to be flexible in handling a reasonably broad class of multi-modality biomedical image analysis and interpretation problems. It provides a relatively general design or planning framework, within which problem specific image analysis and recognition processes can be generated more efficiently and effectively. In this way, generality is gained at the design and planning stages, even though the final implementation stage of interpretation processes is almost invariably problem- and domain-specificIEEE Transactions on Pattern Analysis and Machine Intelligence 11/1995; · 4.80 Impact Factor