Common LISP: the language (2nd ed.)
Common LISP: the language (2nd ed.)
Machine Learning - Special issue on case-based reasoning
Case-based reasoning
Adaptive Reasoning for Real-World Problems: A Schema-Based Approach
Adaptive Reasoning for Real-World Problems: A Schema-Based Approach
Integrating intention and convention to organize problem-solving dialogues
Integrating intention and convention to organize problem-solving dialogues
Organization and retrieval in a conceptual memory for events or CON 51, where are you?
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
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Memory-based reasoning systems are a class of reasoners that derive solutions to new problems based on past experiences. Such reasoners use a long-term memory (LTM) to act as a knowledge base of these past experiences, which may be represented by such things as specific events (i.e. cases), plans, scripts, etc. This paper describes a Unified Long-Term Memory (ULTM) system, which is a dynamic, conceptual memory that was designed to be a general LTM capable of simultaneously supporting multiple intentional reasoning systems. Through a unique mixture of content-independent and domain-specific mechanisms, the ULTM is able to flexibly provide reasoners accurate and timely storage and recall of episodic memory structures. In addition, the ULTM provides support for recognizing opportunities to satisfy suspended goals, allowing reasoning systems to better cope with the unpredictability of dynamic real-world domains by helping them take advantage of unexpected events.