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The action learning approach was originated by Reg Revans. [6] [7] Formative influences for Revan included his time working as a physicist at the University of Cambridge, wherein he noted the importance of each scientist describing their own ignorance, sharing experiences, and communally reflecting in order to learn. [8]
Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic.As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and Filtering) algorithm, [1] which uses agent's observations to construct a long propositional formula over time and subsequently ...
CogniTAO is a decision making engine it based on BDI (belief-desire-intention), it includes built in teamwork capabilities. Soar is a symbolic cognitive architecture. It is based on condition-action rules known as productions. Programmers can use the Soar development toolkit for building both reactive and planning agents or any compromise ...
Sample flowchart representing a decision process when confronted with a lamp that fails to light. In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possible alternative options.
Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes are uncertain. [ 1 ] Originating from operations research in the 1950s, [ 2 ] [ 3 ] MDPs have since gained recognition in a variety of fields, including ecology , economics , healthcare ...
Action research is an interactive inquiry process that balances problem-solving actions implemented in a collaborative context with data-driven collaborative analysis or research to understand underlying causes enabling future predictions about personal and organizational change.
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A learning automaton is an adaptive decision-making unit situated in a random environment that learns the optimal action through repeated interactions with its environment. The actions are chosen according to a specific probability distribution which is updated based on the environment response the automaton obtains by performing a particular ...