ALLIANCE is a software architecture that fa- cilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behavior-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.

Task execution in multi-agent environments may require cooperation among agents. Given a set of agents and a set of tasks which they have to satisfy, we consider situations where each task should be attached to a group of agents that will perform the task. Task allocation to groups of agents is necessary when tasks cannot be performed by a single agent. However it may also be beneficial when groups perform more efficiently with respect to the single agents' performance. In this paper we present several solutions to the problem of task allocation among autonomous agents, and suggest that the agents form coalitions in order to perform tasks or improve the efficiency of their performance. We present efficient distributed algorithms with low ratio bounds and with low computational complexities. These properties are proven theoretically and supported by simulations and an implementation in an agent system. Our methods are based on both the algorithmic aspects of combinatorics and approximat...

In the last few years we have witnessed a surge of business-to-consumer and business-to-business commerce operated on the Internet. However, most current electronic commerce systems are little more than electronic catalogues that allow a user to purchase a product under predetermined and inflexible terms and conditions. We believe that in the next few years we will see a new generation of electronic commerce systems emerge, based on automated negotiation. In this paper, we identify the main parameters on which any automated negotiation depends. To show the applicability of our classification framework, we use it to categorise a representative sample of some of the most prominent negotiation models that exist in the literature.

Autonomous agents working in multi-agent environments may need to cooperate in order to fulfill tasks. Given a set of agents and a set of tasks which they have to satisfy, we consider situations where each task should be attached to a group of agents which will perform the task. The allocation of tasks to groups of agents is necessary when tasks cannot be performed by a single agent. It may also be useful to assign groups of agents to tasks when the group's performance is more efficient than the performance of single agents. In this paper we give an efficient solution to the problem of task allocation among autonomous agents, and suggest that the agents will form coalitions in order to perform tasks or improve the efficiency. We present a distributed algorithm with a low ratio bound and with a low computational complexity. Our algorithm is an any-time algorithm, it is simple, efficient and easy to implement. 1

Abstract Negotiations are very important in a multi-agent environment, particularly, in an environment where there are conflicts between the agents, and cooperation would be beneficial. We have developed a general structure for a Negotiating Automated Agent that consists of five modules: a Prime Minister, a Ministry of Defense, a Foreign Office, a Headquarters and Intelligence. These modules are implemented using a dynamic set of local-agents belonging to the different modules. We used this structure to develop a Diplomacy player, Diplomat. Playing Diplomacy involves a certain amount of technical skills as in other board games, but the capacity to negotiate, explain, convince, promise, keep promises or break them, is an essential ingredient in good play. Diplomat was evaluated and consistently played better than human players.

In the MAS/DAI community, most current work on speech acts focuses on formalizing individual utterances. The next stage will exploit the theory's power to explicate relationships within conversations, or groups of utterances. Computer scientists naturally seek to visualize these relationship in terms of graphs, focusing either on the identities of the individual agents involved or the states through which participating agents move. This paper introduces an alternative formalism, the Dooley Graph. It reviews the kinds of relations that can exist among individual communicative actions (including both speech acts and nonspeech acts), shows the strengths and weaknesses of participant and state graphs, explains the derivation of Dooley Graphs, and suggests their value for designing agents and analyzing the behavior of communities of agents.

Various researchers in Distributed Artificial Intelligence (DAI) have suggested that it would be worthwhile to isolate "aspects of cooperative behavior," general rules that would cause agents to act in ways conducive to cooperation. The hypothesis is that when agents act in certain ways (e.g., share information, act in predictable ways, defer globally constraining choices), it will be easier for them to carry out effective joint action. Another kind of cooperative behavior, less explored in the literature, is when agents independently alter the environment to make it easier for everyone to function effectively. Cooperative behavior of this kind might be to put away a hammer that one finds lying on the floor, knowing that another agent will be able to find it more easily later on. In this paper we are concerned with cooperation of this latter type, state-changing behavior that improves the environment for everyone. We examine the effect that a specific "cooperativity rule" has on agents...

. Autonomous agents are designed to reach goals that were pre-defined by their operators. An important way to execute tasks and to maximize payoff is to share resources and to cooperate on task execution by creating coalitions of agents. Such coalitions will take place if, and only if, each member of a coalition gains more if he joins the coalition than he could gain before. There are several ways to create such coalitions and to divide the joint payoff among the members. Variance in these methods is due to different environments, different settings in a specific environment, and different approaches to a specific environment with specific settings. In this paper we focus on the cooperative (super-additive) environment, and suggest two different algorithms for coalition formation and payoff distribution in this environment. We also deal with the complexity of both computation and communication of each algorithm, and we try to give designers some basic tools for developing agents for th...

Negotiation among multiple agents remains an important topic of research in Distributed Artificial Intelligence (DAI). Most previous work on this subject, however, has focused on bilateral negotiation, deals that are reached between two agents. There has also been research on n-agent agreement which has considered "consensus mechanisms" (such as voting), that allow the full group to coordinate itself. These group decision-making techniques, however, assume that the entire group will (or has to) coordinate its actions. Sub-groups cannot make sub-agreements that exclude other members of the group. In some domains, however, it may be possible for beneficial agreements to be reached among sub-groups of agents, who might be individually motivated to work together to the exclusion of others outside the group. This paper considers this more general case of n-agent coalition formation. We present a simple coalition formation mechanism that uses cryptographic techniques for subadditive Task Or...

We present a general theory that captures the relationship between certain domains and negotiation mechanisms. The analysis makes it possible to categorize precisely the kinds of domains in which agents find themselves, and to use the category to choose appropriate negotiation mechanisms. The theory presented here both generalizes previous results, and allows agent designers to characterize new domains accurately. The analysis thus serves as a critical step in using the theory of negotiation in realworld applications. We show that in certain Task Oriented Domains, there exist distributed consensus mechanisms with simple and stable strategies that lead to efficient outcomes, even when agents have incomplete information about their environment. We also present additional novel results, in particular that in concave domains using all-or-nothing deals, no lying by an agent can be beneficial, and that in subadditive domains, there often exist beneficial decoy lies that do not require full i...