Intelligent, Real-Time Problem Solving. Phase 3

There are many real-time application domains in which the world changes during the problem solving process. Several real-time search algorithms have been proposed for problem solving in dynamic environments. However, there has not been any systematic evaluation and comparison of these algorithms. This paper provides a classification of different dynamic worlds. It then provides a detailed model of a dynamic world where changes occur in edge costs around a zero mean. A formal analysis of the model suggests that the static rank ordering of solution paths is preserved in the proposed dynamic model. The paper provides analysis of two real-time search algorithms, namely DYNORAII and RTA*, for the real-time path planning problem. DYNORAII addresses response-time constraints and dynamic world issues simultaneously. We provide new results on the path planning problem in the proposed dynamic model of graphs. We also provide experimental evaluation of DYNORAII and RTA* in their ability to minimize response-times in dynamic environments.

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Knowledge Management and Problem Solving in Real Time: The Role of Swarm Intelligence

Interdisciplinary Journal of Information Knowledge and Management ◽

10.28945/3528 ◽

2016 ◽

Vol 11 ◽

pp. 177-199 ◽

Cited By ~ 5

Author(s):

Chris W Callaghan

Keyword(s):

Knowledge Management ◽

Problem Solving ◽

Swarm Intelligence ◽

Real Time ◽

Research Problem ◽

Research Process ◽

Population Based ◽

Bee Colony ◽

Time Problem ◽

Societal Problems

Knowledge management research applied to the development of real-time research capability, or capability to solve societal problems in hours and days instead of years and decades, is perhaps increasingly important, given persistent global problems such as the Zika virus and rapidly developing antibiotic resistance. Drawing on swarm intelligence theory, this paper presents an approach to real-time research problem-solving in the form of a framework for understanding the complexity of real-time research and the challenges associated with maximizing collaboration. The objective of this research is to make explicit certain theoretical, methodological, and practical implications deriving from new literature on emerging technologies and new forms of problem solving and to offer a model of real-time problem solving based on a synthesis of the literature. Drawing from ant colony, bee colony, and particle swarm optimization, as well as other population-based metaheuristics, swarm intelligence principles are derived in support of improved effectiveness and efficiency for multidisciplinary human swarm problem-solving. This synthesis seeks to offer useful insights into the research process, by offering a perspective of what maximized collaboration, as a system, implies for real-time problem solving.

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Making the process of strategy choice visible: Inhibition and motor demands impact preschoolers’ real‐time problem solving

Developmental Science ◽

10.1111/desc.13106 ◽

2021 ◽

Author(s):

Aaron DeMasi ◽

Sarah E. Berger

Keyword(s):

Problem Solving ◽

Real Time ◽

Strategy Choice ◽

Time Problem

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