We consider an outsourcing problem where a software agent procures multiple services from providers with uncertain reliabilities to complete a computational task before a strict deadline. The service consumer’s goal is to design an outsourcing strategy (defining which services to procure and when) so as to maximize a specific objective function. This objective function can be different based on the consumer’s nature; a socially-focused consumer often aims to maximize social welfare, while a self-interested consumer often aims to maximize its own utility. However, in both cases, the objective function depends on the providers’ execution costs, which are privately held by the self-interested providers and hence may be misreported to influence the consumer’s decisions. For such settings, we develop a unified approach to design truthful procurement auctions that can be used by both socially-focused and, separately, self-interested consumers. This approach benefits from our proposed weighted threshold payment scheme which pays the provably minimum amount to make an auction with a monotone outsourcing strategy incentive compatible. This payment scheme can handle contingent outsourcing plans, where additional procurement happens gradually over time and only if the success probability of the already hired providers drops below a time-dependent threshold. Using a weighted threshold payment scheme, we design two procurement auctions that maximize, as well as two low-complexity heuristic-based auctions that approximately maximize, the consumer’s expected utility and expected social welfare, respectively. We demonstrate the effectiveness and strength of our proposed auctions through both game-theoretical and empirical analysis.

Supply chain formation (SCF) is the process of determining the set of participants and exchange relationships within a network with the goal of setting up a supply chain that meets some predefined social objective. Many proposed solutions for the SCF problem rely on centralized computation, which presents a single point of failure and can also lead to problems with scalability. Decentralized techniques that aid supply chain emergence offer a more robust and scalable approach by allowing participants to deliberate between themselves about the structure of the optimal supply chain. Current decentralized supply chain emergence mechanisms are only able to deal with simplistic scenarios in which goods are produced and traded in single units only and without taking into account production capacities or input-output ratios other than 1:1. In this paper, we demonstrate the performance of a graphical inference technique, max-sum loopy belief propagation (LBP), in a complex multiunit unit supply chain emergence scenario which models additional constraints such as production capacities and input-to-output ratios. We also provide results demonstrating the performance of LBP in dynamic environments, where the properties and composition of participants are altered as the algorithm is running. Our results suggest that max-sum LBP produces consistently strong solutions on a variety of network structures in a multiunit problem scenario, and that performance tends not to be affected by on-the-fly changes to the properties or composition of participants.

Supply chain formation is the process by which a set of producers within a network determine the subset of these producers able to form a chain to supply goods to one or more consumers at the lowest cost. This problem has been tackled in a number of ways, including auctions, negotiations, and argumentation‐based approaches. In this paper we show how this problem can be cast as an optimization of a pairwise cost function. Optimizing this class of energy functions is NP‐hard but efficient approximations to the global minimum can be obtained using loopy belief propagation (LBP). Here we detail a max‐sum LBP‐based approach to the supply chain formation problem, involving decentralized message‐passing between supply chain participants. Our approach is evaluated against a well‐known decentralized double‐auction method and an optimal centralized technique, showing several improvements on the auction method: it obtains better solutions for most network instances which allow for competitive equilibrium (Competitive equilibrium in Walsh and Wellman is a set of producer costs which permits a Pareto optimal state in which agents in the allocation receive non‐negative surplus and agents not in the allocation would acquire non‐positive surplus by participating in the supply chain) while also optimally solving problems where no competitive equilibrium exists, for which the double‐auction method frequently produces inefficient solutions.

Decentralised supply chain formation involves determining the set of producers within a network able to supply goods to one or more consumers at the lowest cost. This problem is frequently tackled using auctions and negotiations. In this paper we show how it can be cast as an optimisation of a pairwise cost function. Optimising this class of functions is NP-hard but good approximations to the global minimum can be obtained using Loopy Belief Propagation (LBP). Here we detail a LBP-based approach to the supply chain formation problem, involving decentralised message-passing between potential participants. Our approach is evaluated against a well-known double-auction method and an optimal centralised technique, showing several improvements: it obtains better solutions for most networks that admit a competitive equilibriumCompetitive equilibrium as defined in [3] is used as a means of classifying results on certain networks to allow for minor inefficiencies in their auction protocol and agent bidding strategies.

We investigate knowledge exchange among commercial organizations, the rationale behind it, and its effects on the market. Knowledge exchange is known to be beneficial for industry, but in order to explain it, authors have used high-level concepts like network effects, reputation, and trust. We attempt to formalize a plausible and elegant explanation of how and why companies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multiagent model that simulates a market of software providers. Even though the model does not include any high-level concepts, information exchange naturally emerges during simulations as a successful profitable behavior. The conclusions reached by this agent-based analysis are twofold: 1) a straightforward set of assumptions is enough to give rise to exchange in a software market, and 2) knowledge exchange is shown to increase the efficiency of the market.

As information infrastructure move towards open systems where agents come and go, new facilities are required so that these agents can take advantage of each other's functionalities. We need agent systems that can provide to newcomer agents a place and the right agent to interact with. Such functionality must cope with high rate of agent entrance, with high load of agents, with vanishing agents or nodes in the agent system. Given these requirements, agents are constantly facing a problem of deciding where to go and with whom to work with. These two decisions, pertaining to mobility and interaction, have been singled out as fundamental for every agent system. We present an algorithm targeted at these two decisions while it fulfils the aforementioned requirements.

The adoption of agents as utile companions faces the problem of conciliating the development of complex and intelligent functionalities with the requirements of autonomy mobility and adaptability. Our main focus will be on the agents adaptability. A hybrid agent architecture approach is proposed where a static component, which resides at the user's host and includes most of the intelligence and decision support capabilities, is complemented by a mobile component that is aimed at interacting with other agents. Some adaptation strategies, based on classical and fuzzy methodologies, are also discussed using as background scenario a trading market competitive environment with buyer and seller agents interacting in it.

This work attempts to shed light on the fundamental concepts behind the stability of multi-agent systems. We view the system as a discrete time Markov chain with a potentially unknown transitional probability distribution. The system will be considered to be stable when its state has converged to an equilibrium distribution. Faced with the non-trivial task of establishing the convergence to such a distribution, we propose a hypothesis testing approach according to which we test whether the convergence of a particular system metric has occurred. We describe some artificial multi-agent ecosystems that were developed and we present results based on these systems which confirm that this approach qualitatively agrees with our intuition.