Programming expert systems in OPS5: an introduction to rule-based programming
Programming expert systems in OPS5: an introduction to rule-based programming
A general framework for reason maintenance
Artificial Intelligence
Building problem solvers
Using qualitative physics to build articulate software for thermodynamics education
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Putting the Problem Solver Back in the Driver's Seat: Contextual Control of the AMTS
ECAI '90 Workshop on Truth Maintenance Systems
Why dissect a frog when you can simulate a lion?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Efficiency of production systems when coupled with an assumption based truth maintenance system
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
CATMS: an ATMS which avoids label explosions
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
An improved incremental algorithm for generating prime implicates
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Matching 100,045 learned rules
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Reasoning about nonlinear system identification
Artificial Intelligence
Afterword: from this revolution to the next
Smart machines in education
Using Qualitative Physics to Create Articulate Educational Software
IEEE Expert: Intelligent Systems and Their Applications
Why dissect a frog when you can simulate a lion?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Fast context switching in real-time propositional reasoning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Truth maintenance systems provide caches of beliefs and inferences that support explanations and search. Traditionally, the cost of using a TMS is monotonic growth in the size of this cache. In some applications this cost is too high; for example, intelligent learning environments may require students to explore many alternatives, which leads to unacceptable performance. This paper describes an algorithm for fact garbage collection that retains the explanation-generating capabilities of a TMS while eliminating the increased storage overhead. We describe the application context that motivated this work and the properties of applications that benefit from this technique. We present the algorithm, showing how to balance the tradeoff between maintaining a useful cache and reclaiming storage, and analyze its complexity. We demonstrate that this algorithm can eliminate monotonic storage growth, thus making it more practical to field large-scale TMS-based systems.