The question of how humans solve problem has been addressed extensively. However, the direct study of the effectiveness of this process seems to be overlooked. In this paper, we address the issue of the effectiveness of human problem solving: we analyze where this effectiveness comes from and what cognitive mechanisms or heuristics are involved. Our results are based on the optimal probabilistic problem solving strategy that appeared in Solomonoff paper on general problem solving system. We provide arguments that a certain set of cognitive mechanisms or heuristics drive human problem solving in the similar manner as the optimal Solomonoff strategy. The results presented in this paper can serve both cognitive psychology in better understanding of human problem solving processes as well as artificial intelligence in designing more human-like agents.
Current knowledge acquisition tools are oblivious to the process or strategy that the user may be following in entering new knowledge and unaware of their progress during a session. Users have to make up for these shortcomings by keeping track of the status, progress, potential problems and possible courses of actions by themselves. We present a novel extension to existing systems that 1) keeps track of past problem solving episodes and relates them to user entered knowledge, 2) assesses the current status of the knowledge and the problem solving using such relations, and 3) provides assistance to the user based on the assessment. We applied the approach in developing an intelligent assistant for decision making tasks. The resulting interaction shows that the system helps the user understand the progress and guides the knowledge authoring process in terms of making the knowledge more useful, adapting the knowledge to dynamic changes over time, and making the overall problem solving more successful.
This paper provides an overview of a web-based, database-driven cognitive support system for scaffolding ill-structured problem solving processes through fostering self-regulation. Self-regulation learning and ill-structured problem-solving theories guided the design framework of this cognitive tool. Of particular interest are the roles of question prompts, expert view, and peer review mechanisms in supporting self-monitoring, self-regulation, and self-reflection in the processes of ill-structured problem solving, which have been tested through empirical studies in various content domains and contexts. Based on findings, suggestions are made to improve the cognitive support system for future research, including mapping self-regulation learning processes more closely with ill-structured problem-solving processes, and focusing on the system’s capability to automatically adapt scaffolding based on individual needs and prior knowledge.
"We ask first whether we need a theory of creative thinking distinct from a theory of problem solving. Subject to minor qualifications, we conclude there is no such need -- that we call problem solving creative when the problems solved are relatively new and difficult. Next, we summarize what has been learned about problem solving by simulating certain human problem solving processes with digital computers. Finally, we indicate some of the differences in degreee that might be observed in comparing relatively creative with relative routine problem solving."RAND Corporation Paper P-1320, Santa Monica, Calif
Much of the work on ontologies in AI has focused on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. A goal is to make knowledge sharable, by encoding domain knowledge using a standard vocabulary based on the ontology. A parallel attempt at identifying the ontology of problem-solving knowledge would make it possible.to