Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
Coordinating agents in a complex environment is a hard problem, but it can become even harder when certain characteristics of the tasks, like the required number of agents, are un...
– In this paper, we present a hierarchical methodology that learns new walking gaits autonomously while operating in an uncharted environment, such as on the Mars planetary surfa...
This paper presents an agent-based educational environment to teach argument analysis (ALES). The idea is based on the Argumentation Interchange Format Ontology (AIF) using "W...
In recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several a...