Anticipating the actions of others is key to coordinating joint activities. We propose the notion of anticipatory action and perception for for robots acting with humans. We describe four systems in which anticipation has been modeled for human-robot interaction; two in a teamwork setting, and two in a human-robot joint performance setting. In evaluating the effects of anticipatory agent activity, we find in one study that anticipation aids in team efficiency, as well as in the perceived commitment of the robot to the team and its contribution to the team's fluency and success. In another study we see anticipatory action and perception affect the human partner's sense of team fluency, the team's improvement over time, the robot’s contribution to the efficiency and fluency, the robot's intelligence, and the robot’s adaptation to the task. We also find that subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence.

In this paper we describe an original computational model for solving different types of Distributed Constraint Satisfaction Problems (DCSP). The proposed model is called Controller-Agents for Constraints Solving (CACS). This model is intended to be used which is an emerged field from the integration between two paradigms of different nature: Multi-Agent Systems (MAS) and the Constraint Satisfaction Problem paradigm (CSP) where all constraints are treated in central manner as a black-box. This model allows grouping constraints to form a subset that will be treated together as a local problem inside the controller. Using this model allows also handling non-binary constraints easily and directly so that no translating of constraints into binary ones is needed. This paper presents the implementation outlines of a prototype of DCSP solver, its usage methodology and overview of the CACS application for timetabling problems.

We propose a formal framework that supports a model of agent-based Virtual Organisations (VOs) for service grids and provides an associated operational model for the creation of VOs. The framework is intended to be used for describing different service grid applications based on multiple agents and, as a result, it abstracts away from any realisation choices of the service grid application, the agents involved to support the applications and their interactions. Within the proposed framework VOs are seen as emerging from societies of agents, where agents are abstractly characterised by goals and roles they can play within VOs. In turn, VOs are abstractly characterised by the agents participating in them with specific roles, as well as the workflow of services and corresponding contracts suitable for achieving the goals of the participating agents. We illustrate the proposed framework with an earth observation scenario.

We construct a computer model that allows us to simulate the effect of counterinsurgency operations on a population of agents. We build a society of agents who are interconnected in an established social network. Each agent in this network engages in political discourse with other agents over the legitimacy of the existing government. Agents may decide to support an insurgency, join an insurgency, side with the existing government, or remain neutral over which group to support. Using this model we explore the relative importance of social network structure, influence effectiveness, and combat operation effectiveness in minimizing insurgent strength.

Aggregated phenomena in social sciences and economi cs are highly dependent on the way individuals interact. To help understanding the interplay betwe en socio-economic activities and underlying social networks, this paper studies a sequential prisoner's dilemma with binary choice. It proposes an analytical and computational insight about the role of endogenous networks in emergence and sustainability of cooperation and exhibits an alternative to the choice and refusal mechanism that is often proposed to explain cooperation. The study fo cuses on heterogeneous equilibriums and emergence of cooperation from an all-defector state that are the two stylized facts that this model successfully reconstructs.

This thesis aims to provide a foundation for designing computer agents able to work better with people and with other agents in heterogeneous groups. When agents work together on a collaborative activity, in addition to performing their share of the activity, they may be able to help one another and thus improve the collective utility. The thesis specifically focuses on investigating the question of how, when and what kinds of helpful behavior should emerge when agents collaborate, taking into account the costs of a helpful action. It considers collaborative activities that take place in settings in which there is uncertainty about agents' capabilities and about the state of the world. To ensure that helpful behavior improves the overall benefit of the collaboration, the thesis incorporates decision-theoretic mechanisms for managing helpful behavior into existing formalizations of collaborative activity. It provides an investigation of the way people perceive the usefulness of helpful actions when proposed by a computer agent. It proposes incentives for facilitating collaboration among self-interested agents. In addition to these theoretical and empirical contributions, my findings are applied to several real-life application domains with different characteristics.

Agents and market mechanisms are widely elaborated and applied to automate interaction and decision processes among others in robotics, for decentralized control in sensor networks and by algorithmic traders in financial markets. Currently there is a high demand of efficient mechanisms for the provisioning, usage and allocation of distributed services in the Cloud. Such mechanisms and processes are not manually manageable and require decisions taken in quasi real-time. Thus agent decisions should automatically adapt to changing conditions and converge to optimal values. This paper presents a bidding strategy, which is capable of automating the bid generation and utility maximization processes of consumers and providers by the interaction with markets as well as to converge to optimal values. The bidding strategy is applied to the consumer side against benchmark bidding strategies and its behavior and convergence are evaluated in two market mechanisms, a centralized and a decentralized one.

This paper considers the setting wherein a group of agents (e.g., robots) is seeking to obtain a given tangible good, potentially available at different locations in a physical environment. Traveling between locations, as well as acquiring the good at any given location consumes from the resources available to the agents (e.g., battery charge). The availability of the good at any given location, as well as the exact cost of acquiring the good at the location is not fully known in advance, and observed only upon physically arriving at the location. However, a-priori probabilities on the availability and potential cost are provided. Given such as setting, the problem is to find a strategy/plan that maximizes the probability of acquiring the good while minimizing resource consumption. Sample applications include agents in exploration and patrol missions, e.g., rovers on Mars seeking to mine a specific mineral. Although this model captures many real world scenarios, it has not been investigated so far. We focus on the case where locations are aligned along a path, and study several variants of the problem, analyzing the effects of communication and coordination. For the case that agents can communicate, we present a polynomial algorithm that works for any fixed number of agents. For non-communicating agents, we present a polynomial algorithm that is suitable for any number of agents. Finally, we analyze the difference between homogeneous and heterogeneous agents, both with respect to their allotted resources and with respect to their capabilities.

Coalition structure generation has received considerable attention in recent research. Several algorithms have been proposed to solve this problem in Characteristic Function Games (CFGs), where every coalition is assumed to perform equally well in any coalition structure containing it. In contrast, very little attention has been given to the more general Partition Function Games (PFGs), where a coalition's effectiveness may change from one coalition structure to another. In this paper, we deal with PFGs with positive and negative externalities. In this context, we identify the minimum search that is required in order to establish a bound on the quality of the best coalition structure found. We then develop an anytime algorithm that improves this bound with further search, and show that it outperforms the existing state-of-the-art algorithms by orders of magnitude.

Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.