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 and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing

In this paper we propose a two-stage protocol for resource management in a hierarchically organized cloud. The first stage exploits spatial locality for the formation of coalitions of supply agents; the second stage, a combinatorial auction, is based on a modified proxy-based clock algorithm and has two phases, a clock phase and a proxy phase. The clock phase supports price discovery; in the second phase a proxy conducts multiple rounds of a combinatorial auction for the package of services requested by each client. The protocol strikes a balance between low-cost services for cloud clients and a decent profit for the service providers. We also report the results of an empirical investigation of the combinatorial auction stage of the protocol.Comment: 14 page

A Unified Framework for Solving Multiagent Task Assignment Problems

Multiagent task assignment problem descriptors do not fully represent the complex interactions in a multiagent domain, and algorithmic solutions vary widely depending on how the domain is represented. This issue is compounded as related research fields contain descriptors that similarly describe multiagent task assignment problems, including complex domain interactions, but generally do not provide the mechanisms needed to solve the multiagent aspect of task assignment. This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains. Ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation are combined to form the basis of the constrained multiagent task scheduling (CMTS) problem. Basic analysis reveals the exponential size of the solution space for a CMTS problem, approximated by O(2n(m+n)) based on the number of agents and tasks involved in a problem. The shape of the solution space is shown to contain numerous discontinuous regions due to the complexities involved in relational constraints defined between agents and tasks. The CMTS descriptor represents a wide range of classical and modern problems, such as job shop scheduling, the traveling salesman problem, vehicle routing, and cooperative multi-object tracking. Problems using the CMTS representation are solvable by a suite of algorithms, with varying degrees of suitability. Solution generating methods range from simple random scheduling to state-of-the-art biologically inspired approaches. Techniques from classical task assignment solvers are extended to handle multiagent task problems where agents can also multitask. Additional ideas are incorporated from constraint satisfaction, project scheduling, evolutionary algorithms, dynamic coalition formation, auctioning, and behavior-based robotics to highlight how different solution generation strategies apply to the complex problem space

From Marriages to Coalitions: A Soft CSP Approach

In this workwerepresent the Optimal Stable Marriage problem as a Soft Constraint Satisfaction Problem. In addition, we extend this problem from couples of individuals to coalitions of generic agents, in order to define new coalition-formation principles and stability conditions. In the coalition case, we suppose the preference value as a trust score, since trust can describe a nodes belief in another nodes capabilities, honesty and reliability. Soft constraints represent a general and expressive framework that is able to deal with distinct concepts of optimality by only changing the related c-semiring structure, instead of using di erent ad-hoc algorithms. At last, we propose an implementation of the classical OSM problem by using Integer Linear Programming tools

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac