Automated Qualitative Domain Abstraction

Automated problem-solving for engineered devices is based on models that capture the essential aspects of their behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal is to determine partitions for the domains of the variables (termed qualitative values) that are both necessary and sufficient for the task at hand. We present a formalization of the problem within a common relational (constraint-based) framework, present results regarding solutions to task-dependent qualitative domain abstraction, and devise methods for automatically determining qualitative values for a device model.

[1]  Eugene C. Freuder.  Eliminating Interchangeable Values in Constraint Satisfaction Problems , 1991, AAAI.

[2]  Johan de Kleer,et al.  Readings in qualitative reasoning about physical systems , 1990 .

[3]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[4]  M. Weitzman.  Optimal search for the best alternative , 1978 .

[5]  Brian Falkenhainer,et al.  Compositional Modeling: Finding the Right Model for the Job , 1991, Artif. Intell..

[6]  Kishor S. Trivedi.  Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .

[7]  Munindar P. Singh,et al.  Service-Oriented Computing: Semantics, Processes, Agents , 2010 .

[8]  Somesh Jha,et al.  Query restart strategies for Web agents , 1998, AGENTS '98.

[9]  Peter Struss,et al.  A Prototype for Model-based On-board Diagnosis of Automotive Systems , 2000, AI Commun..

[10]  Alon Y. Halevy,et al.  Automated Model Selection for Simulation Based on Relevance Reasoning , 1997, Artif. Intell..

[11]  Benjamin Kuipers,et al.  Qualitative and Quantitative Simulation: Bridging the Gap , 1997, Artif. Intell..

[12]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[13]  Martin Sachenbacher.  Automated qualitative abstraction and its application to automotive systems , 2001 .

[14]  Rina Dechter,et al.  Network-Based Heuristics for Constraint-Satisfaction Problems , 1987, Artif. Intell..

[15]  Luca Console,et al.  Readings in Model-Based Diagnosis , 1992 .

[16]  A. Mas-Colell,et al.  Microeconomic Theory , 1995 .

[17]  Georg Gottlob,et al.  A Comparison of Structural CSP Decomposition Methods , 1999, IJCAI.

[18]  Peter Struss.  Automated Abstraction of Numerical Simulation Models - Theory and Practical Experience , 2003 .

[19]  Pietro Torasso,et al.  Merging Indiscriminable Diagnoses: An Approach Based on Automatic Domains Abstraction , 2002 .

[20]  Rina Dechter,et al.  Network-based heuristics for constraint satisfaction problems , 1988 .

[21]  P. Pandurang Nayak,et al.  Causal Approximations , 1992, Artif. Intell..

[22]  Benjamin Kuipers,et al.  Qualitative Simulation , 1986, Artificial Intelligence.

[23]  Benjamin J. Kaipers,et al.  Qualitative Simulation , 1989, Artif. Intell..

[24]  Bruce W. Porter,et al.  Automated Modeling of Complex Systems to Answer Prediction Questions , 1997, Artif. Intell..

[25]  B. Moldovanu,et al.  Efficient Design with Interdependent Valuations , 2001 .