Decision making must be made within an appropriate context; we contend that such context is best represented by a hierarchy of states. The lowest levels of this hierarchy represent the observed raw data, or specific low-level behaviors and decisions. As we ascend the hierarchy, the states become increasingly abstract, representing higher order tactics, strategies, and over-arching mission goals...

in recent years, researches on reinforcement learning (rl) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. neural network reinforcement learning (nnrl) is among the most popular algorithms in the rl framework. the advantage of using neural networks enables the rl to search for optimal policies more efficiently in several real-life applicat...

objective: the purpose of this study was to identify reasons domestic violence occurs within intimate relationships. methods: the target group was female victims and male offenders. the offenders group consisted of 25 men from a batterer’s intervention group. the victims group composed of 9 women from center against spouse abuse (casa) intervention group. results: domestic violence occurred at ...

We introduce the notion of an “adaptive growth process” in order to explain an experimental result from the 1950s in which a complex mechanism capable of distinguishing between two sounds emerges from a homogeneous chemical solution. We present a very simple computational model which exhibits an adaptive growth process. Adaptive growth processes could have practical applications in adaptive con...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

nowadays, outsourcing is viewed as a trade strategy and organizations tend to adopt new strategies to achieve competitive advantages in the current world of business. focusing on main copmpetencies, and transferring most of activities to outside resources of organization( outsourcing) is one such strategy is. in this paper, we aim to decide on decision maker agent of transportation system, by a...

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting do...

Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts whi...

The existing reinforcement learning methods have been seriously suffering from the curse of dimension problem especially when they are applied to multiagent dynamic environments. One of the typical examples is a case of RoboCup competitions since other agents and their behavior easily cause state and action space explosion. This paper presents a method of modular learning in a multiagent enviro...