Amal El Fallah Seghrouchni

Multi-Agent Programming

ion keeping the agent abstraction level e.g. no agents sharing and calling OO objects effective programming models for controllable and observable computational entities Modularity away from the monolithic and centralised view Orthogonality wrt agent models, architectures, platforms support for heterogeneous systems

Languages, Methodologies and Development Tools for Multi-Agent Systems

In what case do you like reading so much? What about the type of the languages methodologies and development tools for multi agent systems book? The needs to read? Well, everybody has their own reason why should read some books. Mostly, it will relate to their necessity to get knowledge from the book and want to read just to get entertainment. Novels, story book, and other entertaining books become so popular this day. Besides, the scientific books will also be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.

A Context-Aware Multi-Agent System as a Middleware for Ambient Intelligence

There is currently a lot of work in Ambient Intelligence—or AmI—reporting on specific scenarios, or on implementations of particular cases. In the same time, there is a common agreement of the fact that AmI applications should be pervasive, covering a large number of devices, assisting a large number of people, and serving a large number of purposes. In an attempt to achieve scalable scenarios and implementations, we have focused our research on the development of a generic middleware layer for the context-aware transfer and exchange of information between devices. This paper presents a model for a such middleware, based on software agents, in which context-awareness is implemented both in the agent’s representation of context information, and in the logical topology of the agent system. The model is oriented towards decentralization of the system and relies mostly on local behavior. The paper also reports on several proof-of-concept applications that have been developed and tested using the proposed model, proving thus the validity of the approach.

Ao dai: agent oriented design for ambient intelligence

In this paper we present mobile Multi-Agent Systems (MAS) as a specific paradigm to design intelligent and distributed applications in the context of Ambient Intelligence (AmI). We discuss how mobility, coupled with MAS, can be useful to meet the requirements of AmI. Indeed, the main features of mobile MAS, such as natural distribution of the system, inherent intelligence of the agents, and their mobility help to address a large scope of distributed applications in the domain of AmI. Other features of MAS, like multi-agent planning, context-awareness and self-adaptation are also very useful to bring an added value to AmI applications. They allow the implementation of both intelligent and collaborative agent behavior. This paper presents the Ao Dai project, that employs the mobile MAS paradigm, and serves as a prototype AmI environment. We also illustrate the functioning of the application through a scenario of user guidance in a smart environment.

A Higher-Order Agent Model with Contextual Planning Management for Ambient Systems

This paper presents a concrete software architecture dedicated to ambient intelligence (AmI) features and requirements. The proposed behavioral model, called Higher-order Agent (HoA) captures the evolution of the mental representation of the agent and the one of its plan simultaneously. Plan expressions are written and composed using a formal algebraic language, namely AgLOTOS, so that plans are built automatically and on the fly, as a system of concurrent processes. Based on a specific semantics, a guidance service is also proposed to assist the agent in its execution. Moreover due to the specific structure of AgLOTOS expressions, the update of sub-plans is realized automatically accordingly to the revising of intentions, hence maintaining the consistency of the agent.

Learning better together

This article addresses collaborative concept learning in a MAS. In a concept learning problem an agent incrementally revises a hypothetical representation of some target concept to keep it consistent with the whole set of examples that it receives from the environment or from other agents. In the program SMILE, this notion of consistency was extended to a group of agents. A surprising experimental result of that work was that a group of agents learns better the difficult boolean problems, than a unique agent receiving the same examples. The first purpose of the present paper is to propose some explanation about such unexpected superiority of collaborative learning. Furthermore, when considering large societies of agents, using pure sequential protocols is unrrealistic. The second and main purpose of this paper is thus to propose and experiment broadcast protocols for collaborative learning.

Efficient Verification of MASs with Projections

Constrained global types are a powerful means to represent agent interaction protocols. In our recent research we used them to represent complex protocols in a very compact way, and we exploited them to dynamically verify actual agents’ interactions with respect to different protocols in both Jason and JADE. The main drawback of our previous approach is the full centralization of the monitoring activity, which is delegated to a unique monitor agent in charge of verifying that the messages exchanged among all the agents are compliant with the protocol. This approach works well for MASs with few agents, but could become unsuitable in communication-intensive and highly-distributed MASs where hundreds of agents should be monitored.

Graphs and Patterns for Context-Awareness

A central issue in the domain of Ambient Intelligence is context - awareness. While previous research in the field presents complex context-aware infrastructures, but with little flexibility and fixed context representations, this paper presents a simple, flexible and decentralized representation of context, for the detection of appropriate context-aware action. This representation is inspired from notions like concept maps and conceptual graphs. A formalism for context patterns, that allows the detection and solution of problems, based on the user’s context, is also proposed.

Multi-Agent Systems: A Paradigm to Design Ambient Intelligent Applications

In this paper we present a Multi-Agent System (MAS) paradigm and discuss how it can be used to design intelligent and distributed systems. The main features of this MAS, such as natural distribution of the system, inherent intelligence of its agents, and their mobility help address a large scope of distributed applications including the domain of ambient intelligence. Other features of the MAS, like multiagent planning, context-awareness and adaptation are also very useful since they bring added value, by allowing to implement intelligent and collective behavior. The paper also presents a scenario of ambient intelligence and shows how it could be designed using the MAS paradigm.

The Open System Setting in Timed Multiagent Patrolling

everal techniques have been proposed in the last few years to address the multiagent patrolling task. They share the assumption of a closed system setting (the set of agents present in the system is constant, no agent joins or leaves), which is a strong requirement and limits the applicability of multiagent patrolling models. In this article, we propose to revisit some of the techniques proposed in the literature to adapt them to the open society setting, and to compare their performances on a simple scenario where an agent decides to quit the patrolling task.