![]() Studying means-ends analysis won't improve your ability to solve algebra word problems very much. In other words, problem solving expertise tends to be specific to a domain. One finding of research in problem solving (AI and cognitive science) is that general strategies are weak compared to strategies that use context-specific information. One heuristic for selecting an operation (transition) is to choose one that makes the greatest reduction in distance between the current state and the goal state.Īnother heuristic is to work backwards from the goal state (the "working backwards" strategy). Often, solving a few sub-problems will make it easy to solve the whole problem. Then the problem solver tries to find the path between the start and goal states of the sub-problem. To define a sub-problem, one of the intermediate states is treated as a temporary goal state and another is treated as a temporary start state. Means-ends analysis is a method described by Simon where the problem solver tries to divide the problem into smaller problems (sub-problems). The black lines represent possible transitions (operations) between states. The diagram below shows a state space containing a start state and a goal state. For example, see this web page about how computers can solve the eight puzzle. To solve the problem is to find a sequence of operations that will allow one to move from the start state to the goal state. The operations implicitly define a "state-space" including the start and goal states. Among his many contributions were general problem solving strategies used in Artificial Intelligence (AI).Ī problem can be defined as a start state, a goal state, and a set of possible operations. Herbert Simon won the Nobel prize in economics in 1979 for research in human decision making. Problem Solving Methods Developed for Artificial Intelligence ![]() This frees cognitive resources for the deeper aspects of the problem.Įxample: Musicians practice to automatize the mechanical aspects of performance so they can concentrate on interpretating the music with deep emotional expression. * Monitor and correct their own performance more effectively.īy repeated practice, experts have automatized response patterns and chunked information into more complex patterns. * Spend greater portion of time in problem analysis phase. * Perceive large, meaningful patterns in given information. Experts Versus Novices (Chi, Glaser, & Farr, 1988) ![]() Problems that can only be solved by heuristics are ill-defined, poorly structured problems. Problems that can be solved by algorithms are well-defined, well-structured problems. Heuristics = Guidelines that are not guaranteed to produce the solution. "There is sufficient research evidence to make any reasonable person skeptical about the benefits of discovery learning.Overall, the constructivist view of learning may be best supported by methods of instruction that involve cognitive activity rather than behavioral activity, instructional guidance rather than pure discovery, and curricular focus rather than unstructured exploration."Īlgorithms = Rigid procedures that always produce the solution. Pure discovery learning, in which children are provided materials but no learning goals or scaffolding, rarely works. The exemplar view is a third theory of concepts, according to which the person remembers a number of examples for each concept. New instances are judged according to how similar they are to the prototype. According to the prototype view, humans store a single prototype (typical example) for each concept. However, the classical view can not account for fuzzy concepts, dogs with three legs, etc.Īnother approach is the prototype view of concepts. For example, the concept of "dog" might be represented by: According to the classical view, concepts are mentally represented by abstract rules that are learned. Work by Jerome Bruner led to the "classical view" of concepts. Complex Cognitive Processes Concept LearningĮxamples of items allowed and not allowed in a library.
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