Ping Xuan

Communication decisions in multi-agent cooperation: model and experiments

In multi-agent cooperation, agents share a common goal, which is evaluated through a global utility function. However, an agent typically cannot observe the global state of an uncertain environment, and therefore they must communicate with each other in order to share the information needed for deciding which actions to take. We argue that, when communication incurs a cost (due to resource consumption, for example), whether to communicate or not also becomes a decision to make. Hence, communication decision becomes part of the overall agent decision problem. In order to explicitly address this problem, we present a multi-agent extension to Markov decision processes in which communication can be modeled as an explicit action that incurs a cost. This framework provides a foundation for a quantified study of agent coordination policies and provides both motivation and insight to the design of heuristic approaches. An example problem is studied under this framework. From this example we can see the impact communication policies have on the overall agent policies, and what implications we can find toward the design of agent coordination policies.

Diagnosis as an integral part of multi-agent adaptability

Agents working under real world conditions may face an environment capable of changing rapidly from one moment to the next, either through perceived faults, unexpected interactions or adversarial intrusions. The members of a multi-agent system can gracefully and efficiently handle such situations by adapting, either by evolving internal structures and behavior or repairing or isolating those external influences believed to be malfunctioning. The first step in achieving adaptability is diagnosis-being able to accurately detect and determine the cause of a fault based on its symptoms. In this paper we examine how domain independent diagnosis plays a role in multi-agent systems, including the information required to support and produce diagnoses. Particular attention is paid to coordination based diagnosis directed by a causal model. Several examples are described in the context of an Intelligent Home environment, and the issue of diagnostic sensitivity versus efficiency is addressed.

Multi-agent policies: from centralized ones to decentralized ones

In this paper we divide multi-agent policies into two categories: centralized ones and decentralized ones. They reflect different views of multi-agent systems and different decision-theoretic underpinnings. While the centralized policies specify the decision of the agents according to the global system state, the decentralized policies, which correspond to the decisions of situated agents, must assume only a partial knowledge of the system in each agent and must deal with communication explicitly. In this paper we relate these two types of policies by introducing a formal and systematic methodology for transforming centralized policies into a variety of decentralized policies. We introduce a set of transformation strategies, and provide a representation for discussing decentralized communication decisions. Through our experiments, we show that our methodology enables us to derive a class of interesting policies that have a range of expected utilities and amount of communication, and allows us to gain important insights into decentralized coordination strategies from a decision-theoretic perspective.

The UMASS intelligent home project

Intelligent environments are an interesting development and research application problem for multi-agent systems. The functional and spatial distribution of tasks naturally lends itself to a multi-agent model and the existence of shared resources creates interactions over which the agents must coordinate. In the UMASS Intelligent Home project we have designed and implemented a set of distributed autonomous home control agents and deployed them in a simulated home environment. Our focus is primarily on resource coordination, though this project has multiple goals and areas of exploration ranging from the intellectual evaluation of the application as a general MAS testbed to the practical evaluation of our agent building and simulation tools.

Incorporating Uncertainty in Agent Commitments

Commitments play a central role in multi-agent coordination. However, they are inherently uncertain and it is important to take these uncertainties into account during planning and scheduling. This paper addresses the problem of handling the uncertainty in commitments. We propose a new model of commitment that incorporates the uncertainty, the use of contingency analysis to reduce the uncertainty, and a negotiation framework for handling commitments with uncertainty.

Investigating Interactions between Agent Conversations and Agent Control Components

\begin{abstract} Exploring agent conversation in the context of fine-grained agent coordination research has raised several intellectual questions. The major issues pertain to interactions between different agent conversations, the representations chosen for different classes of conversations, the explicit modeling of interactions between the conversations, and how to address these interactions. This paper is not so ambitious as to attempt to address these questions, only frame them in the context of quantified, scheduling-centric multi-agent coordination. research. \end{abstract}

Communication in multi-agent Markov decision processes

In this paper, we formulate agents decision process under the framework of Markov decision processes, and in particular, the multi-agent extension to Markov decision process that includes agent communication decisions. We model communication as the way for each agent to obtain local state information in other agents, by paying a certain communication cost. Thus, agents have to decide not only which local action to perform, but also whether it is worthwhile to perform a communication action before deciding the local action. We believe that this would provide a foundation for formal study of coordination activities and may lead to some insights to the design of agent coordination policies, and heuristic approaches in particular. An example problem is studied under this framework and its implications to coordination are discussed.