8,394 research outputs found
Multiagent negotiation for fair and unbiased resource allocation
This paper proposes a novel solution for the n agent cake cutting (resource allocation) problem. We propose a negotiation protocol for dividing a resource among n agents and then provide an algorithm for allotting portions of the resource. We prove that this protocol can enable distribution of the resource among n agents in a fair manner. The protocol enables agents to choose portions based on their internal utility function, which they do not have to reveal. In addition to being fair, the protocol has desirable features such as being unbiased and verifiable while allocating resources. In the case where the resource is two-dimensional (a circular cake) and uniform, it is shown that each agent can get close to l/n of the whole resource.Utility theory ; Utility function ; Bargaining ; Artificial intelligence ; Resource allocation ; Multiagent system
Agent-Based Distributed Resource Allocation in Continuous Dynamic Systems
Intelligent agents and multiagent systems reveal new strategies to design highly flexible automation systems. There are first promising industrial applications of multiagent systems for the control of manufacturing, logistics, traffic or multi-robot systems. One reason for the success of most of these applications is their nature as some form of a distributed resource allocation problem which can be addressed very well by multiagent systems. Resource allocation problems solved by agents can be further categorized into static or dynamic problems. In static problems, the allocations do not depend on time and many resource allocation problem of practical interest can be solved using these static considerations, even in discrete-event systems like manufacturing or logistic systems. However, problems especially in highly dynamic environments cannot be addressed by this pure static approach since the allocations, i.e. the decision variables, depend on time and previous states of the considered system. These problems are hardly considered in the relevant agent literature and if, most often only discrete-event systems are considered.
This work focuses on agent-based distributed dynamic resource allocation problems especially in continuous production systems or other continuous systems. Based on the current states of the distributed dynamic system, continuous-time allocation trajectories must be computed in real-time. Designing multiagent systems for distributed resource allocation mainly comprises the design of the local capabilities of the single agents and the interaction mechanisms that makes them find the best or at least a feasible allocation without any central control. In this work, the agents are designed as two-level entities: while the low-level functions are responsible for the real-time allocation of the resources in the form of closed-loop feedback control, the high-level functionalities realize the deliberative capabilities such as long-term planning and negotiation of the resource allocations. Herein, the resource allocation problem is considered as a distributed optimization problem under certain constraints. The agents play the role of local optimizers which then have to coordinate their local solutions to an overall consistent solution.
It is shown in this contribution that the described approach can be interpreted as a market-based allocation scheme based on balancing of supply and demand of the resources using a virtual price. However, the agents calculate and negotiate complete supply and demand trajectories using model-based predictions which also leads to the calculation of a price trajectory. This novel approach does not only consider the dynamic behaviour of the distributed system but also combines control tasks and resource allocation in a very consistent way. The approach is demonstrated using two practical applications: a heating system and an industrial sugar extraction process
Negotiating Socially Optimal Allocations of Resources
A multiagent system may be thought of as an artificial society of autonomous
software agents and we can apply concepts borrowed from welfare economics and
social choice theory to assess the social welfare of such an agent society. In
this paper, we study an abstract negotiation framework where agents can agree
on multilateral deals to exchange bundles of indivisible resources. We then
analyse how these deals affect social welfare for different instances of the
basic framework and different interpretations of the concept of social welfare
itself. In particular, we show how certain classes of deals are both sufficient
and necessary to guarantee that a socially optimal allocation of resources will
be reached eventually
Nash Social Welfare in Multiagent Resource Allocation
We study different aspects of the multiagent resource allocation problem when the objective is to find an
allocation that maximizes Nash social welfare, the product of the utilities of the individual agents. The
Nash solution is an important welfare criterion that combines efficiency and fairness considerations. We
show that the problem of finding an optimal outcome is NP-hard for a number of different languages for
representing agent preferences; we establish new results regarding convergence to Nash-optimal outcomes
in a distributed negotiation framework; and we design and test algorithms similar to those applied in
combinatorial auctions for computing such an outcome directly
Modelling Multilateral Negotiation in Linear Logic
We show how to embed a framework for multilateral negotiation,
in which a group of agents implement a sequence of deals
concerning the exchange of a number of resources, into linear logic.
In this model, multisets of goods, allocations of resources, preferences
of agents, and deals are all modelled as formulas of linear logic.
Whether or not a proposed deal is rational, given the preferences of
the agents concerned, reduces to a question of provability, as does
the question of whether there exists a sequence of deals leading to an
allocation with certain desirable properties, such as maximising social
welfare. Thus, linear logic provides a formal basis for modelling
convergence properties in distributed resource allocation
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
Multiagent Maximum Coverage Problems: The Trade-off Between Anarchy and Stability
The price of anarchy and price of stability are three well-studied
performance metrics that seek to characterize the inefficiency of equilibria in
distributed systems. The distinction between these two performance metrics
centers on the equilibria that they focus on: the price of anarchy
characterizes the quality of the worst-performing equilibria, while the price
of stability characterizes the quality of the best-performing equilibria. While
much of the literature focuses on these metrics from an analysis perspective,
in this work we consider these performance metrics from a design perspective.
Specifically, we focus on the setting where a system operator is tasked with
designing local utility functions to optimize these performance metrics in a
class of games termed covering games. Our main result characterizes a
fundamental trade-off between the price of anarchy and price of stability in
the form of a fully explicit Pareto frontier. Within this setup, optimizing the
price of anarchy comes directly at the expense of the price of stability (and
vice versa). Our second results demonstrates how a system-operator could
incorporate an additional piece of system-level information into the design of
the agents' utility functions to breach these limitations and improve the
system's performance. This valuable piece of system-level information pertains
to the performance of worst performing agent in the system.Comment: 14 pages, 4 figure
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