Learning in multi-agent environments constitutes a research and application area whose importance is broadly acknowledged in artificial intelligence. There is a rapidly growing body of literature on multi-agent learning. In this paper, the multi-agent learning methods in an uncertain environment are addressed. The presented methods are not exhaustive, but they highlight the major methods used b...

this article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for reinforcement learning agents. these definitions are used as a tool of knowledge transfer among agents. the agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. in other words, the agents are assumed t...

Koffka Khan Department of Computing and Information Technology The University of the West Indies, Trinidad and Tobago, W.I Email: [email protected] Wayne Goodridge Department of Computing and Information Technology The University of the West Indies, Trinidad and Tobago, W.I Email: [email protected] -------------------------------------------------------------------ABSTRACT----...

We present a new algorithm, GM-Sarsa(O), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processes. According to our formulation different sub-goals are modeled as MDPs that are coupled by the requirement that they share actions. Existing reinforcement learning algorithms address similar problem formulations by fir...

We examine methods for on-line optimization of the basis function for temporal difference Reinforcement Learning algorithms. We concentrate on architectures with a linear parameterization of the value function. Our methods optimize the weights of the network while simultaneously adapting the parameters of the basis functions in order to decrease the Bellman approximation error. A gradient-based...

This study addresses the question of whether or not active learning can be taught online. There are many definitions of learning: It is the process and the sum total of acquiring knowledge, skills, attitudes, values, beliefs, and emotions. There is, however, a nuanced definition of active online learning, defined as methods by which learners actively participate in the learning process (e.g., o...

The center of spatio-temporal representation for own body and its surrounding space is supposed at the parietal cortex in human brains, but the mechanism how the brain computes them is still not clearly understood though its hierarchical representation is expected. One of such hierarchical models, this paper propose a method which integrates multimodal information based on the Slow Feature Anal...

|Blackjack or twenty-one is a card game where the player attempts to beat the dealer, by obtaining a sum of card values that is equal to or less than 21 so that his total is higher than the dealer's. The probabilistic nature of the game makes it an interesting testbed problem for learning algorithms, though the problem of learning a good playing strategy is not obvious. Learning with a teacher ...

Advances in the technology along with reduction in processor size, its memory, and wireless antenna size has facilitated the construction of low cost, low powered and multifunctional Sensor nodes which in turn led to high demand for development of Wireless Sensor Networks. A lot of research work has been done regarding the development of routing protocols for WSNs. This paper provides a brief o...

Investigating language acquisition is one of the most challenging problems in the area of studying language. Syllable learning as a level of language acquisition has a considerable significance since it plays an important role in language acquisition. Because of impossibility of studying language acquisition directly with children, especially in its developmental phases, computer models will be...