Tomas Klos

A specification of the Agent Reputation and Trust (ART) testbed: experimentation and competition for trust in agent societies

A diverse collection of trust-modeling algorithms for multi-agent systems has been developed in recent years, resulting in significant breadth-wise growth without unified direction or benchmarks. Based on enthusiastic response from the agent trust community, the Agent Reputation and Trust (ART) Testbed initiative has been launched, charged with the task of establishing a testbed for agent trust- and reputation-related technologies. This testbed serves in two roles: (1) as a competition forum in which researchers can compare their technologies against objective metrics, and (2) as a suite of tools with flexible parameters, allowing researchers to perform customizable, easily-repeatable experiments. This paper first enumerates trust research objectives to be addressed in the testbed and desirable testbed characteristics, then presents a competition testbed specification that is justified according to these requirements. In the testbeds artwork appraisal domain, agents, who valuate paintings for clients, may gather opinions from other agents to produce accurate appraisals. The testbeds implementation architecture is discussed briefly, as well.

Agent-based computational transaction cost economics ☆

This article explores the use of ‘agent-based computational economics’ (ACE) for modelling the development of transactions between firms. Transaction cost economics neglects learning and the development of trust, ignores the complexity of multiple agents, and assumes rather than investigates the efficiency of outcomes. Efficiency here refers to minimum cost or maximum profit. We model how co-operation and trust emerge and shift adaptively as relations evolve in a context of multiple, interacting agents. This may open up a new area of application for the ACE methodology. A simulation model is developed in which agents make and break transaction relations on the basis of preferences, based on trust and potential profit. Profit is a function of product differentiation, specificity of assets, economy of scale and learning by doing in ongoing relations. Agents adapt their trust in a partner as a function of his loyalty, exhibited by his continuation of a relation. They also adapt the weight they attach to trust on the basis of realized profit. The model enables an assessment of the efficiency of outcomes relative to the optimum, as a function of trust and market conditions. We conduct a few experiments to illustrate this application of ACE.

Distributed task allocation in social networks

This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NP-hard. Moreover, it is not approximable within some factor. We develop an algorithm based on the contract-net protocol. Our algorithm is completely distributed, and it assumes that agents have only local knowledge about tasks and resources. We conduct a set of experiments to evaluate the performance and scalability of the proposed algorithm in terms of solution quality and computation time. Three different types of networks, namely small-world, random and scale-free networks, are used to represent various social relationships among agents in realistic applications. The results demonstrate that our algorithm works well and that it scales well to large-scale applications.

On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences

We study the problem of allocating a set of indivisible goods to a set of agents having additive preferences. We introduce two new important complexity results concerning efficiency and fairness in resource allocation problems: we prove that the problem of deciding whether a given allocation is Pareto-optimal is coNP-complete, and that the problem of deciding whether there is a Pareto-efficient and envy-free allocation is

The agent reputation and trust (ART) testbed

\Sigma_2^p

Decentralized Interaction and Co-Adaptation in the Repeated Prisoner‘sDilemma

-complete.

Flexibility and decoupling in Simple Temporal Networks

The Agent Reputation and Trust (ART) Testbed initiative has been launched with the goal of establishing a testbed for agent reputation- and trust-related technologies. The art Testbed serves in two roles: (1) as a competition forum in which researchers can compare their technologies against objective metrics, and (2) as a suite of tools with flexible parameters, allowing researchers to perform customizable, easily-repeatable experiments. In the Testbeds artwork appraisal domain, agents, who valuate paintings for clients, may purchase opinions and reputation information from other agents to produce accurate appraisals. The art Testbed features useful data collection tools for storing, downloading, and replaying game data for experimental analysis.

Automated Interactive Sales Processes

A Prisoner&2018;s dilemma that is repeated indefinitely has many equilibria; the problem of selecting among these is often approached using evolutionary models. The background of this paper is a number of earlier studies in which a specific type of evolutionary model, a genetic algorithm (GA), was used to investigate which behavior survives under selective pressure. However, that normative instrument searches for equilibria that may never be attainable. Furthermore, it aims for optimization and, accordingly, says what people should do to be successful in repeated prisoner&2018;s dilemma (RPD) type situations. In the current paper, I employ simulation to find out what people would do, whether this makes them successful or not. Using a replication of Miller&2018;s (1988) GA study for comparison, a model is simulated in which the population is spatially distributed across a torus. The agents only interact with their neighbors and locally adapt their strategy to what they perceive to be successful behavior among those neighbors. Although centralized GA-evolution may lead to somewhat better performance, this goes at the cost of a large increase in required computations while a population with decentralized interactions and co-adaptation is almost as successful and, additionally, endogenously learns a more efficient scheme for adaptation. Finally, when the agents&2018; perceptive capabilities are limited even further, so that they can only perceive how their neighbors are doing against themselves, rather than against all those neighbors&2018; opponents&2014;which essentially removes reputation as a source of information&2014;cooperation breaks down.

Aiding Human Reliance Decision Making Using Computational Models of Trust

We propose a new metric to determine the flexibility of a Simple Temporal Network (STN). After reviewing some existing flexibility metrics, we conclude that these metrics fail to capture the dependencies between events specified in the STN. As a consequence, these metrics will usually overestimate the available flexibility in such a system. We propose to use an intuitively more acceptable flexibility metric. This metric is based upon the notion of an interval schedule for an STN. Such an interval schedule specifies an interval for every event in the STN in such a way that, for every event, we are free to choose a starting time within its interval independently from the choice made for other events. We show that an interval schedule that maximizes our flexibility metric is computable in low-order polynomial time. As byproducts of this flexibility metric, we discuss simple solutions to problems in STNs with uncertainty (STNUs) and temporal decoupling in STNs. With respect to the latter we show that after computing our flexibility metric, we get a decomposition of the STN almost for free. Even more importantly, we show that contrary to popular belief, such a decomposition does not affect the flexibility of the original STN.

Evolutionary dynamics of regret minimization

The paper mentions that an automated interactive sales process can combine aggregate anonymous knowledge of customer preferences with specific data about the ongoing negotiation process with the current customer.