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Human problem solving provides a structure for addressing the academic or behavioral problem. Problem solving requires full collaboration among individual daily life. The purpose of Problem solving is to put in place a decision making process that will lead to development of instructional & Intervention strategies with high probability of success. Objective of the study are to construct the Human Problem Solving Space Detection Test. To establish the reliability of Human Problems Solving Space Detection Test Discipline wise the present study is developmental in nature. The purpose of the study was to develop Problem Solving Detection Test and standardize it. Problem Solving Space Detection Test Corporating state of knowledge component included 36 items. Field wise the Problem Solving Space Detection Test was highly reliable.

Problem solving is a crucial higher-order thinking ability of humans. Humans' ability to solve problems is a critical higher-order thinking ability. Mathematical problem solving, analogical problem solving, complex problem solving, situated problem solving, and so on are all examples of problem solving. Furthermore, distinct types of research analysis, models, and theories are based on the mechanisms and elements involved in diverse problem-solving types. The conventional approach to understanding human problem solving is a representation-laden description, which is similar to most cognitive explanations of psychological processes. On the other hand, the paper goes beyond representational theories and models to investigate nonrepresentational and situated aspects of human problem solving. Problem solving is a crucial higher-order thinking ability of humans. The paper is a rudimentary attempt to present a nonrepresentational, Affordance-Situation-Attunement (ASA) framework of human problem solving. The aim is to invoke ASA as an alternative framework, in contrast with the dominant representational explanation of human problem solving. The aim is not to disparage the representational theories and models of problem solving but to contribute a nonrepresentational working framework and elements for highlighting the situated nature of human problem solving. Keywords: Problem solving, affordances, embodied cognition, situated cognition, ecological psychology

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them are not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and AI planners, we found that when facing manageable problems that require planning to a certain number of subgoals (from 1 to 6), participants make an adaptive use of their cognitive resources – namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. When facing more challenging problems that require planning up to 7 or 8 subgoals, participants tend to select a simpler (greedy) strategy wherein plans consider only the next 1 or 2 subgoals. Furthermore, we found a strong similarity between how different participants solve the same problems. These results support the view of problem solving as a bounded rational process, which adapts costly cognitive resources to task demands.Significance statementProblem-solving usually requires planning ahead for several steps. While previous studies assessed that we can plan multiple steps ahead during problem solving, it is unclear whether we use planning resources parsimoniously, given their significant cognitive costs. Theories of bounded rationality suggest that rather than always operating at our maximum capacity, we should adapt planning depth to task demands. Alternatively, each of us could have a preferred “planning depth” and use the same depth, regardless of the problem. By comparing the behaviour of human and AI planners during problem solving, we found that people adapt planning depth to problem complexity; but resort to simpler strategies when the problem is very hard. Our study reveals a flexible use of bounded cognitive resources during problem solving.

Human problem-solving is a fundamental yet complex phenomena; it has fascinated and attracted a lot of researchers to understand, and theorize about it. Modeling and simulating human problem-solving played a pivotal role in Herbert Simon’s research program. Herbert Simon (along with Allen Newell and Cliff Shaw) was among the pioneers of artificial intelligence, by interlinking cognitive psychology, economics, philosophy, and computer science through the research work on human problem-solving. His curiosity and work focused not on replicating the brain but to understand and replicate human problem solving techniques (which he often referred to as programs/algorithms). The main aim of this article is to trace the origins and paths that enabled Simon (Newell and Shaw) to further develop his (their) original ideas.

We harness the ability of people to perceive and interact with visual patterns in order to enhance the performance of a machine learning method. We show how we can collect evidence about how people optimize the parameters of an ensemble classification system using a tool that provides a visualization of misclassification costs. Then, we use these observations about human attempts to minimize cost in order to extend the performance of a state-of-the-art ensemble classification system. The study highlights opportunities for learning from evidence collected about human problem solving to refine and extend automated learning and inference.

PROBLEM-SOLVING THEORIES IN THREE AREAS - TRADITIONAL (STIMULUS-RESPONSE) LEARNING, COGNITIVE-GESTALT APPROACHES, AND COMPUTER AND MATHEMATICAL MODELS - WERE SUMMARIZED. RECENT EMPIRICAL STUDIES (1960-65) ON PROBLEM SOLVING WERE CATEGORIZED ACCORDING TO TYPE OF BEHAVIOR ELICITED BY PARTICULAR PROBLEM-SOLVING TASKS. ANAGRAM, "INSIGHT,""WATER-JAR," AND ARITHMETIC PROBLEMS WERE CONSIDERED SOLVABLE BY COVERT TRIAL-AND-ERROR BEHAVIOR. IT WAS SUGGESTED THAT "SWITCH-LIGHT," CLASSIFICATION, PROBABILITY-LEARNING, AND NUMEROUS "MISCELLANEOUS" TASKS SHOULD BE APPROACHED BY OVERT TRIAL-AND-ERROR METHODS. BY DISCUSSING PROBLEM SOLVING IN TERMS OF OVERT VERSUS COVERT TRIAL-AND-ERROR BEHAVIOR, THE REPORT PRESENTED A NEW APPROACH TO THE STUDY OF HUMAN PROBLEM SOLVING THROUGH THE USE OF CATEGORICAL IDENTIFICATION OF PROBLEM-SOLVING TASKS. (GC)

A theory that there is a correspondence between Piagetian conservation operations and groups of symmetry transformations, and that these symmetry transformations may be used in explaining human problem solving behaviors, is developed in this paper. Current research in artificial intelligence is briefly reviewed, then details of the symmetry transformation theory are given along with examples of its application. The Tower of Hanoi Problem is extensively analyzed as an illustration of the theory. (DT)

This paper suggests that Herbert Simon’s concept of proof and predictions, in the solution of problems by human’s, considered as Information Processing Agents subject to boundedly rational behaviour and satisficing objectives, is to be interpreted in terms of constructive mathematics.

The effectiveness of a workbook for training creative thinking, "Thinking Creatively: A Guide to Training Imagination," was evaluated with a sample of 198 inner-city students. The materials seek to teach attitudes which predispose an individual to behave more creatively and techniques for producing new combinations of ideas. Two sixth-grade classes (one from a low- and one from a medium-ability school) and two eighth-grade classes (one low- and one medium-ability) served as experimental groups. Four similar classes comprised the control groups. Three subtests from the Torrance Tests of Creative Thinking were administered to all subjects (Ss) as pretests. After about four weeks of training (one class hour on each of about 20 days), another form of the Torrance Test was given, along with a 20-item attitude questionnaire for all Ss, plus two other instruments just for students and teachers in experimental classes. Despite the finding that both the training materials and the testing instruments were difficult for many of the Ss to read and thoroughly comprehend, most students and teachers felt that students had benefited from the creativity training experience. Two experimental classes showed modest gains in Torrance Test scores. (Author/JM)