Parallelisation and application of AD3 as a method for solving large scale combinatorial auctions

Auctions, and combinatorial auctions (CAs), have been successfully employed to solve coordination problems in a wide range of application domains. However, the scale of CAs that can be optimally solved is small because of the complexity of the winner determination problem (WDP), namely of finding the bids that maximise the auctioneer’s revenue. A way of approximating the solution of a WDP is to solve its linear programming relaxation. The recently proposed Alternate Direction Dual Decomposition algorithm (AD3) has been shown to ef- ficiently solve large-scale LP relaxations. Hence, in this paper we show how to encode the WDP so that it can be approximated by means of AD3. Moreover, we present PAR-AD3, the first parallel implementation of AD3. PAR-AD3 shows to be up to 12.4 times faster than CPLEX in a single-thread execution, and up to 23 times faster than parallel CPLEX in an 8-core architecture. Therefore PAR- AD3 becomes the algorithm of choice to solve large-scale WDP LP relaxations for hard instances. Furthermore, PAR-AD3 has potential when considering large- scale coordination problems that must be solved as optimisation problems.Research supported by MICINN projects TIN2011-28689-C02-01, TIN2013-45732-C4-4-P and TIN2012-38876-C02-01Peer reviewe

Trust in Multiagent Systems

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings

Persuasive negotiation for autonomous agents: A rhetorical approach

Persuasive negotiation occurs when autonomous agents exchange proposals that are backed up by rhetorical arguments (such as threats, rewards, or appeals). The role of such rhetorical arguments is to persuade the negotiation opponent to accept proposals more readily. To this end, this paper presents a rhetorical model of persuasion that defines the main types of rhetorical particles that are used and that provides a decision making model to enable an agent to determine what type of rhetorical argument to send in a given context and how to evaluate rhetorical arguments that are received. The model is empirically evaluated and we show that it is effective and efficient in reaching agreements

Trust-Based Mechanism Design

We define trust-based mechanism design as an augmentation of traditional mechanism design in which agents take into account the degree of trust that they have in their counterparts when determining their allocations. To this end, we develop an efficient, individually rational, and incentive compatible mechanism based on trust. This mechanism is embedded in a task allocation scenario in which the trust in an agent is derived from the reported performance success of that agent by all the other agents in the system. We also empirically study the evolution of our mechanism when iterated and show that, in the long run, it always chooses the most successful and cheapest agents to fulfill an allocation and chooses better allocations than other comparable models when faced with biased reporting

Near-optimal anytime coalition structure generation

Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best set of agents that should participate in a given team. To this end, in this paper, we present a novel, anytime algorithms designed for this purpose. Our algorithm can generate solutions that either have a tight bound from the optimal or are optimal (depending on the objective) and works by partitioning the space in terms of a small set of elements that represent structures which contain coalitions of particular sizes. It then performs an online heuristic search that prunes the space and only considers valid and non-redundant coalition structures. We empirically show that we are able to find solutions that are, in the worst case, 99% efficient in 0.0043% of the time to find the optimal value by the state of the art dynamic programming (DP) algorithm (for 20 agents), using 33% less memory

Trust evaluation through relationship analysis

Current mechanisms for evaluating the trustworthiness of an agent within an electronic marketplace depend either on using a history of interactions or on recommendations from other agents. In the first case, these requirements limit what an agent with no prior interaction history can do. In the second case, they transform the problem into one of trusting the recommending agent. However, these mechanisms do not consider the relationships between agents that arise through interactions (such as buying or selling) or through overarching organisational structures (such as hierarchical or flat), which can also aid in evaluating trustworthiness. In response, this paper outlines a method that enables agents to evaluate the trustworthiness of their counterparts, based solely on an analysis of such relationships. Specifically, relationships are identified using a generic technique in conjunction with a basic model for agent-based marketplaces. They are then interpreted through a trust model that enables the inference of trust valuations based on the different types of relationships. In this way, we provide a further component for a trust evaluation model that addresses some of the limitations of existing work

Minimising intrusiveness in pervasive computing environments using multi-agent negotiation

This paper highlights intrusiveness as a key issue in the field of pervasive computing environments and presents a multi-agent approach to tackling it. Specifically, we discuss how interruptions can impact on individual and group tasks and how they can be managed by taking into account user and group preferences through negotiation between software agents. The system we develop is implemented on the Jabber platform and is deployed in the context of a meeting room scenario

C-Link: A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation

Coalition formation is a fundamental approach tomulti-agent coordination. In this paper we addressthe specific problem of coalition structure gener-ation, and focus on providing good-enough solu-tions using a novel heuristic approach that is basedon data clustering methods. In particular, we pro-pose a hierarchical agglomerative clustering ap-proach (C-Link), which uses a similarity criterionbetween coalitions based on the gain that the sys-tem achieves if two coalitions merge. We empir-ically evaluate C-Link on a synthetic benchmarkdata-set as well as in collective energy purchasingsettings. Our results show that the C-link approachperforms very well against an optimal benchmarkbased on Mixed-Integer Programming, achievingsolutions which are in the worst case about 80%of the optimal (in the synthetic data-set), and 98%of the optimal (in the energy data-set). Thus weshow that C-Link can return solutions for problemsinvolving thousands of agents within minutes