Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent’s confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach—conducting multiple multi-issue negotiations—with its peers. Otherwise, it uses a pipelined approach—conducting multiple single-issue negotiations—with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent’s confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness

Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent’s confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach—conducting multiple multi-issue negotiations—with its peers. Otherwise, it uses a pipelined approach—conducting multiple single-issue negotiations—with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent’s confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness

Coalition Formation under Uncertainty

Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents from the collective and they constrain the environment to limit the sources of uncertainty. A modeling approach and algorithm for coalition formation is presented that decreases the collective\u27s dependence on knowing agent types. The agent modeling approach enforces stability, allows for arbitrary expansion of the collective, and serves as a basis for calculation of individual coalition payoffs. It explicitly captures uncertainty in agent type and allows uncertainty in coalition value and agent cost, and no agent in the collective is required to perfectly know another agents type. The modeling approach is incorporated into a two part algorithm to generate, evaluate, and join stable coalitions for task execution. A comparison with a prior approach designed to handle uncertainty in agent type shows that the protocol not only provides greater flexibility, but also handles uncertainty on a greater scale. Additional results show the application of the approach to real-world robotics and demonstrate the algorithm\u27s scalability. This provides a framework well suited to decentralized task allocation in general collectives

Beyond Goldwater-Nichols

This report culminated almost two years of effort at CSIS, which began by developing an approach for both revisiting the Goldwater-Nichols Department of Defense Reorganization Act of 1986 and for addressing issues that were beyond the scope of that landmark legislation

Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition

Dynamic Coalition Formation Under Uncertainty

Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty while allowing the agent types to fall outside of a known set. The agent types are captured through a novel agent modeling technique that handles uncertainty through a belief-based evaluation mechanism. This technique allows for uncertainty in environmental data, agent type, coalition value, and agent cost. An investigation of both the effects of adding agents on processing time and of model quality on the convergence rate of initial agent models (and thereby coalition quality) is provided. This approach handles uncertainty on a larger scale than previous work and provides a mechanism readily applied to a dynamic collective of real-world robots. Abstract © IEEE

Decentralised Coordination in RoboCup Rescue

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the ?res which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long range communication devices. Against this background, we provide a novel decentralised solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a Coalition Formation with Spatial and Temporal constraints (CFST) problem where agents form coalitions in order to complete tasks, each with different demands. In order to design a decentralised algorithm for CFST we formulate it as a Distributed Constraint Optimisation problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralised message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralised algorithms used for this problem