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Finally, we discuss extensions to other types of machine learning and problem solving systems.
Problem solving systems typically comprise many agents, each characterized by at least partial autonomy.
A challenge for problem solving systems is to coordinate system agent operations during problem solving.
This paper describes how sociotechnological systems comprising human and technological agents can be considered problem solving systems.
It is therefore not surprising that tutorial dialogue systems are closer to commercial practicality, with demonstrated learning benefits relative to conventional instruction in various evaluations, than collaborative problem solving systems for realistic applications.
However, we should immediately note that collaborative problem solving systems typically deal with much less predictable domain situations and user inputs than tutorial systems, and accordingly the former place much greater emphasis on flexible dialogue handling than the latter.
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Our results are based on the optimal probabilistic problem solving strategy that appeared in Solomonoff paper on general problem solving system.
If an adaptable problem solving system is in place, the incentives necessary to bring out and replicate success are in material terms fairly minor, but they must be aligned correctly.
As a consistent problem solving system, algorithms based on multi-criteria decision-making are regarded to be simple and clear, suitable to substantiate solutions as well as easily applied in practise.
Domain-general systems that the functional reading system may draw upon include specific sensory systems, fine motor systems for the mouth and hand, attentional systems, networks of supervisory executive functions, the limbic system, and the higher-level thinking and problem solving system.
Evolutionary algorithms (EAs) are general-purpose stochastic search methods that use the metaphor of evolution as the key element in the design and implementation of computer-based problems solving systems.
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