Multiagent learning is a promising direction of the modern and future research in the context of intelligent systems. While the single-agent case has been well studied in the last two decades, the multiagent case has not been broadly studied due to its complexity. When several autonomous agents learn and act simultaneously, the environment becomes strictly unpredictable and all assumptions that...

Recent literature in multiagent systems show a significant increase in interest and research on normative systems which are defined as (Boella, Torre, & Verhagen, 2008): A normative multiagent system is a multiagent system organized by means of mechanisms to represent, communicate, distribute, detect, create, modify, and enforce norms, and mechanisms to deliberate about norms and detect norm vi...

This paper provides an overview of the work being done at the Air Force Institute of Technology on the Multiagent Systems Engineering methodology and the associated agentTool environment. Our research is focused on discovering methods and techniques for engineering practical multiagent systems. It uses the abstraction provided by multiagent systems for developing intelligent, distributed softwa...

— Researchers designing the multiagent tools and techniques for CSCL environments are often faced with high cost, time, and effort required to investigate the effectiveness of their tools and techniques in large-scale and longitudinal studies in a real-world environment containing human users. Here, we propose SimCoL, a multiagent environment that simulates collaborative learning among students...

Open giant intelligent information system has been studied as a new field of complex intelligent information systems. In this paper, we mainly discuss about system design of this type of complex software systems. We first introduce system complexities, and then we discusse the applicability of multiagent-based approach for building open giant intelligent information system. A multiagent system ...

In the last years, multiagent systems have been proposed to solve a wide range of applications in distributed contexts, as they provide many advantages, such as parallelism, robustness, and scalability. A key element for the performance and reliability of the multiagent system is to design carefully the interaction and synchronization among the agents. Particularly, debugging and monitoring mul...

We analyze the problem of modeling and developing multiagent systems from the organizational theory point of view. In particular, we focus on the critical issue of adapting multiagent systems organizations whenever changes in the structure of the multiagent system are required. We survey different relevant agent-oriented methodologies and discuss their suitability in dealing with adaptation in ...

We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the condit...