Embedded Mission Decision-Making based on Dynamic Decision Networks in SoPC Platform

Computational modeling of dynamic decision making using connectionist networks

In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decisio...

Mission-based decision-making for boards.

Dynamic Decision Making 1 108 Dynamic Decision Making

This section reviews a specialty within the field of decision-making known as dynamic decision-making. Dynamic decisions are characterized by a decision-maker choosing among various actions at different points in time in order to control and optimize performance of a dynamic stochastic system. Realistic examples include fighting fires, navigational control, battlefield decisions, medical emerge...

Instance-based learning in dynamic decision making

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refin...

Knowledge-based Modeling in Dynamic Decision Making

A knowledge-based model that emulates human behavior in a Dynamic Decision Making task is proposed. The model, MAIDEN-DSF, uses a connectionist representation of knowledge and a value function to compute the best alternative. In order to validate MAIDEN-DSF, two data sets have been used: a training set and a test set that contain the behavior of participants that performed the task with differe...