We propose group communication for agent coordination within “active rooms” and other pervasive computing scenarios featuring strict real-time requirements, inherently unreliable communication, and a large but continuously changing set of context-aware autonomous systems. Messages are exchanged over multicast channels, which may remind of chat rooms in which everybody hears everything being said. The issues that have to be faced (e.g., changing users’ preferences and locations; performance constraints; redundancies of sensors and actuators; agents on mobile devices continuously joining and leaving) require the ability of dynamically selecting the “best” agents for providing a service in a given context. Our approach is based on the idea of implicit organization, which refers to the set of all agents willing to play a given role on a given channel. An implicit organization is a special form of team with no explicit formation phase and a single role involved. No middle agent is required. A set of protocols, designed for unreliable group communication, are used to negotiate a coordination policy, and for team coordination. Preconditions and effects of these protocols are formalized by means of the joint intention theory (JIT).

In order to construct and deploy large-scale multi-agent systems, we must address one of the fundamental issues of distributed systems, the possibility of partial failures. This means that faulttolerance is an inevitable issue for large-scale multi-agent systems. In this paper, we discuss the issues and propose an approach for fault-tolerance of multi-agent systems. The starting idea is the application of replication strategies to agents, the most critical agents being replicated to prevent failures. As criticality of agents may evolve during the course of computation and problem solving, and as resources are bounded, we need to dynamically and automatically adapt the number of replicas of agents, in order to maximize their reliability and availability. We will describe our approach and related mechanisms for evaluating the criticality of a given agent (based on application-level semantic information, e.g. interdependences, and also system-level statistical information, e.g., communication load) and for deciding what strategy to apply (e.g., active replication, passive) how to parameterize it (e.g., number of replicas). We also will report on experiments conducted with our prototype architecture (named DimaX). 1.

Overhearing is gaining attention as a generic method for cooperative monitoring of distributed, open, multiagent systems. It involves monitoring the routine conversations of agents–who know they are being overheard–to assist the agents, assess their progress, or suggest advice. While there have been several investigations of applications and methods of overhearing, no formal model of overhearing exists. This paper takes steps towards such a model. It first formalizes a conversation system–the set of conversations in a multi-agent system. It then defines a key step in overhearing–conversation recognition– identifying the conversations that took place within a system, given a set of overheard messages. We provide a skeleton algorithm for conversation recognition, and provide instantiations of it for settings involving no message loss, random message loss, and systematic message loss (such as always losing one side of the conversation). We analyze the complexity of these algorithms, and show that the systematic message loss algorithm, which is unique to overhearing, is significantly more efficient then the random loss algorithm (which is intractable). 1.

Abstract. We are investigating how to provide intelligent, pervasive support of group of people within so-called “smart environments”. Our current main assumption, based on literature in psychology and organizational studies, is that a group performs some complex, routine task as a structured activity, that is, by following some protocols that allow its members to coordinate and share a common understanding about the current progress towards the group’s goal and the roles currently played by each member. If this is the case, a condition to provide support to a group activity by artificial agents is to share the same understanding. To this end, we have identified two initial goals: first, being able to understand if a group activity is progressing with respect to its expected evolution, by analyzing what is happening within the smart environment; second, recognizing what are the social roles of the group members, taking in mind that these are not necessarily pre-assigned and may change in time. This paper sketches a preliminary approach to these issues and a computational model for an overhearer agent. We suggest a preliminary set of rules for conversation analysis and social role recognition, and validate them against the simple case of implicit organizations, which – being artificial – follow well-known protocols.

When we model and analyze trust in organizations or information systems we have to take into account two di#erent levels of analysis: social and individual. Social levels define the structure of organizations, whereas individual levels focus on individual agents. This is particularly important when capturing security requirements where a "normally" trusted organizational role can be played by an untrusted individual. Our goal

Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent’s preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by incrementally using plan knowledge and observations to change state probabilities. We also discuss the work and compare it with other probabilistic models in the paper.

Integrating security concerns throughout the whole software development process is one of today's challenges in software and requirements engineering research. A challenge that so far has proved difficult to meet. The major difficulty

Research on execution monitoring in its own is still not very common within the field of robotics and autonomous systems. It is more common that researchers interested in control architectures or execution planning include monitoring as a small part of their work when they realize that it is needed. On the other hand, execution monitoring has been a well studied topic within industrial control, although control theorists seldom use this term. Instead they refer to the problem of fault detection and isolation (FDI). This survey will use the knowledge and terminology from industrial control in order to classify different execution monitoring approaches applied to robotics. The survey is particularly focused on autonomous mobile robotics. © 2005 Elsevier B.V. All rights reserved.

Today within the AAMAS community, we see at least four competing approaches to building multiagent systems: beliefdesire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic approaches. While there is exciting progress within each approach, there is a lack of cross-cutting research. This paper highlights hybrid approaches for multiagent teamwork. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. While our early work was inspired by BDI, we will present an overview of recent research that uses DCOPs and distributed POMDPs in building agent teams. While DCOP and distributed POMDP algorithms provide promising results, hybrid approaches help us address problems of scalability and expressiveness. For example, in the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance. We present some recent results from applying this approach in a Disaster Rescue simulation domain being developed with help from the Los

Abstract. The behaviours of autonomous agents may deviate from those deemed to be for the good of the societal systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, and may be encoded in electronic contracts in order to specify the obliged, permitted and prohibited behaviours of agents that are signatories to such contracts. Enactment and management of electronic contracts thus enables the use of regulatory mechanisms to ensure that agent behaviours comply with the encoded norms. To facilitate such mechanisms requires monitoring in order to detect and explain violation of norms. In this paper we propose a framework for monitoring that is to be implemented and integrated into a suite of contract enactment and management tools. The framework adopts a non-intrusive approach to monitoring, whereby the states of a contract with respect to its contained norms can be inferred on the basis of messages exchanged. Specifically, the framework deploys agents that observe messages sent between contract signatories, where these messages correspond to agent behaviours and therefore indicate whether norms are, or are in danger of, being violated. 1