Using A Probabilistic Agent To Support Learning In Small Groups

Abstract

Social relationships are highly complex activities that are very difficult to model computationally. In order to represent these relationships, we may consider various aspects of the individual, such as affective state, psychological issues, and cognition. We may also consider social aspects, as how people relate to each other, and to what group they belong. Intelligent Tutoring Systems, Multi-agent Systems and Affective Computing are research areas which our research group have been investigating, in order to improve individual and collaborative learning. This paper focuses on a Social Agent which has been modelled using probabilistic networks and acts in an educational application. Using the Social Agent as a testbed, we present a way to perform the deliberation process in BDI and Bayesian Networks (BN). The assemblage of mental states and Bayesian Networks is done by viewing beliefs as networks, and desires and intentions as particular chance variable states that agents pursue. In this work, we are particularly concerned with the deliberation about which states of affairs the agent will intend. The focus of this paper is on how to build a real application by using the deliberation process developed in one of our previous work.

Validating a Novel Conflict Resolution Strategy Selection Method (ConfRSSM) Via Multi-Agent Simulation

“…Studies in multi-agent systems include the inquiry for rational, autonomous and flexible behavior of entities, and their interaction and coordination in different areas [14]. The foundation of multi-agent systems play a significant role in the growth of teaching systems, because the basic issues of teaching and learning could be easily resolved by multi-agent systems [15].…”