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Title: IN PROBLEM-SOLVING Authors: Abstract: Main Research Question: How can memory be modeled as a control structure in problem-solving? Methodology: Results: Implications: Conclusion: Summary: The main research question of this study was to model memory as a control structure in problem-solving. The authors achieved this by delineating a categorical formalization of memory as a control structure driving performance in inference systems. They abstracted away control mechanisms from three widely used representations of memory in cognitive systems (scripts, production rules, and clusters) and explained how categorical triples capture the interaction between learning and problem-solving. The study found that memory systems must have the ability to cope with new information and be altered by every experience they process. Reminding, a critical feature of memory systems, occurs when the memory system has found the most appropriate structure in memory that will help in processing a new input. The authors proposed the reminding as computation thesis, suggesting that reminding and processing itself ought to amount to different views of the same mechanism. The implications of this research are significant for the design of memory systems and the understanding of cognitive processes. It provides a framework for studying the performance of artificial intelligence inference and cognitive systems, grounded in category theory. The study suggests that memory can be modeled as a monadic control construct, which can be applied to the investigation of the role of reminding in problem-solving and the design of expert memory systems. Link to Article: https://arxiv.org/abs/0402035v1 Authors: arXiv ID: 0402035v1 [[Category:Computer Science]] [[Category:Memory]] [[Category:Systems]] [[Category:Problem]] [[Category:Solving]] [[Category:As]]
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