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...

The agent/multi-agent system paradigm is an important field of Artificial Intelligence. The use of this paradigm in real-world problems is one of the main lines of interest in this area. To do this, it is necessary to make use of agent/multi-agent architectures and artefacts. This paper describes an architecture for real-time agents, an artefact (InSiDE) for the specification of agentbased syst...

the health industry’s supply chain is full of uncertainty and unpredictability. thus, building an intelligent system to effectively capture the requirements of customers and help manage the supply chain is very important. despite artificial intelligence widespread acceptance as a decision-aid tool, ai has seen limited application in supply chain management. to fully exploit the potential benefi...

This paper is devoted to exploring the relationships between computational agents, as they can be found in multi-agent systems (MAS) or Distributed Artificial Intelligence (DAI), and the different techniques regrouped under the generic appellation “multi-agent based simulation” (MABS). Its main purpose is to show that MABS, despite its name, is in fact rarely based on computational agents. We b...

Introduction Learning modalities System architecture Agent specification Tutor agent Tutor assistant agent Learner personal agent Steps towards a user comprehensive model Conclusions Acknowledgements References

For effective use of agent communities in extended problem-solving sessions, humans must be able to both guide agent operations and understand agent progress. This paper presents a framework for directability of a community of agents by a human supervisor that focuses on three dimensions: adjustable agent autonomy, strategy preference, and community-level constraints. The paper also describes a...

Agent-based routing in wireless ad hoc networks defines a set of rules that all the participating nodes follow. Routing becomes a collaboration between nodes, reducing computational and resource costs. Swarm Intelligence uses agent-like entities from insect societies as a metaphor to solve the routing problem. Certain insects exchange information about their activities and the environment in wh...

Since the early eighties, Distributed Artificial Intelligence emerged as a promising area for modelling and implementing distributed computational processes and entities displaying some kind of intelligence. This paper presents our perspective of the potential application of Multi-Agent Systems together with some ideas on the relevant research that makes those applications possible. A more exte...

Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well collaborating and exploiting collective intelligence. This article considers a group autonomous agents learning track the same given reference trajectory in possibly small number trials. We propose novel control method that combines iterative (ILC) with up...

Introduction Thanks to recent advances in the field of distributed artificial intelligence, agent-based models (ABM) can now be used to run simulations of social phenomena based on their computerized representations, and to apply experimental methods in social sciences (Axelrod 1997, Gilbert and Troitzsch 1999, Jager 2000). In the field of renewable resource management and environmental science...