Model-Based Reasoning
Functional Models of Selective Attention and Context Dependency
Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism ofthese functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency withinthe realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature onfunctional models of selective attention and context dependency.
Model-Based Scientific Discovery: A Study in Space Bioengineering
The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. It works iteratively until the results of the proposed experiment can be modeled.
Symbolic Model Checking
Kluwer. See also: Symbolic Model Checking: An Approach to the State Explosion Problem. Doctoral thesis, Carnegie Mellon University, 1992 (http://www.kenmcmil.com/pubs/thesis.pdf). J.R. Burch, E.M. Clarke, K.L. McMillan, D.L. Dill, L.J. Hwang, Symbolic model checking: 1020 States and beyond, Information and Computation, Volume 98, Issue 2, June 1992, Pages 142-170 (http://www.sciencedirect.com/science/article/pii/089054019290017A). Burch, J. R.; Clarke, E.M.; McMillan, K. L.; Dill, D.L., Sequential circuit verification using symbolic model checking, Design Automation Conference, 1990. Proceedings, 27th ACM/IEEE, vol., no., pp.46,51, 24-28 Jun 1990. (https://ieeexplore.ieee.org/document/114827) Burch, J.R.; Clarke, E.M.; Long, D.E.; McMillan, K.L.; Dill, D.L., Symbolic model checking for sequential circuit verification, Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, vol.13, no.4, pp.401,424, Apr 1994 (https://ieeexplore.ieee.org/document/275352). E. M. Clarke, O. Grumberg, K. L. McMillan, and X. Zhao. 1995. Efficient generation of counterexamples and witnesses in symbolic model checking. In Proceedings of the 32nd annual ACM/IEEE Design Automation Conference (DAC '95). ACM, New York, NY, USA, 427-432 (http://dl.acm.org/citation.cfm?id=217565). Burch, Jerry R.; Clarke, Edmund M.; Long, David E.; McMillan, Kenneth L.; and Dill, David L., Symbolic Model Checking for Sequential Circuit Verification. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems, Vol. 13, No. 4, pp. 401-424, April 1994 (http://www.cs.cmu.edu/~emc/papers/Conference%20Papers/Sequential%20circuit%20verification%20using%20symbolic%20model%20checking.pdf).
National Aeronautics and Space Administration Workshop on Monitoring and Diagnosis
De Kleer agreed Institute for the Learning Sciences, university laboratories to real-world that model construction can be difficult Troy Heindel of the Gensym Corporation development efforts, state-of-the-art but noted that the overhead involved (formerly of NASA Johnson research in model-based reasoning in developing structural, Space Center [JSC]), Ben Kuipers of (MBR), and an overview of relevant functional, or causal models is not the University of Texas at Austin, research and applications activities in worse than that associated with developing Ethan Scarl of Boeing Computing the European Space Agency (ESA).
Integrating Case-Based and Model-Based Reasoning: A Computational Model of Design Problem Solving
My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.
Integrating Case-Based and Model-Based Reasoning: A Computational Model of Design Problem Solving
My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.
A qualitative physics based on confluences
Artificial Intelligence 24:7—83. Reprinted In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commonsense World, chap. 4, pp. 109–183. Ablex.