In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

Existing personalisation approaches, such as collaborative filtering or content based recommendations, are highly dependent on the domain and/or the source of the data. Therefore, there is a need for more accurate means to capture and model the interests of the user across domains, and to interlink them in a semantically-enhanced interest graph. We propose a new approach for multi-source, cross-genre recommendations that can exploit the heterogeneous nature of user profile data, which has been aggregated from multiple personalised web services, such as blogs, wikis and microblogs. Our approach is based on the Spreading Activation model that exploits intrinsic links between entities across a number of data sources. The proposed method is highly customizable and applicable both to generic and specific recommendation scenarios and use cases. With the growing number of Social Web applications in the enterprise (blogs, wikis, micro blogging, etc.), it becomes difficult for knowledge workers to avoid content overload and to quickly identify relevant people, communities and information. We demonstrate the application of our approach in an industrial use case that involves recommendation of social semantic data across multiple services in a distributed collaborative environment.

Service composition, which achieves the goal of value-added services, has been considered as the core technique of Service-oriented Computing (SOC). To cope with the challenge of ever-increasing number of web services, graph-based web service network has emerged as a potential solution to the state of art SOC. In such a way, composite services are constructed by applying searching algorithms to the built graph, and proved to achieve outstanding performance in complexity. However, web service network suffers two crucial disadvantages: poor connectivity and negative links, and both of them have crucial negative impact on service composition. To cope with the problems, we propose two methods in this paper. Firstly, leveraging social network analysis, we focus on enriching web service network by adding valuable services, which will play positive roles in solving poor connective problem. Secondly, we show a serious status that numerous negative links contained in the underlying networks, and then we propose to identify and remove the negative links based on users’ historical composite services.

Defender-Attacker Stackelberg games are the foundations of toolsdeployed for computing optimal patrolling strategies in adversarialdomains such as the United states Federal Air Marshals Service and the UnitedStates Coast Guard, among others.In Stackelberg game models of these systems the attacker knows only theprobability that each target is covered by the defender, but isoblivious to the detailed timing of the coverage schedule.In many real-world situations, however, the attacker can observe thecurrent location of the defender and can exploit this knowledge toreason about the defender's future moves.We study Stackelberg security games in which the defender sequentiallymoves between targets, with moves constrained by an exogenouslyspecified graph, while the attacker can observe the defender's currentlocation and his (stochastic) policy concerning future moves. We offerfive contributions: (1) We model this adversarial patrolling game (APG) as a stochastic game with special structure and presentseveral alternative formulations that leverage the general non-linearprogramming (NLP) approach for computing equilibria in zero-sumstochastic games. We show that our formulations yield significantlybetter solutions than previous approaches. (2) We extend theNLP formulation for APG allow for attacks that may take multiple timesteps to unfold.(3) We provide anapproximate MILP formulation that uses discrete defender moveprobabilities. (4) We experimentally demonstrate the efficacy of anNLP-based approach, and systematically study the impact of networktopology on the results.(5) We extend our model to allow the defender to construct the graph constraining his moves, at some cost, and offer novel algorithms for this setting, finding that a MILP approximation is much more effective than the exact NLP in this setting.

Market or auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents are willing to cooperate and can be trusted to perform assigned tasks. Reciprocal collaboration may not always be a valid assumption. In cases where auctions are used for task allocation, without explicit revenue exchange, incentives are needed to enforce cooperation. An approach to incentive based trust is presented, which enables detection of team members that are not contributing and for dynamic formation of teams.

Game theoretic predictions about equilibrium behavior depend upon assumptions of inflexibility of belief, of accord between belief and choice, and of choice across situations that share a game-theoretic structure. However, researchers rarely possess any knowledge of the actual beliefs of subjects, and rarely compare how a subject behaves in settings that share game-theoretic structure but that differ in other respects. Our within-subject experiments utilize a belief elicitation mechanism, roughly similar to a prediction market, in a laboratory setting to identify subjects’ beliefs about other subjects’ choices and beliefs. These experiments additionally allow us to compare choices in different settings that have similar game-theoretic structure. We find first, as have others,that subjects’ choices in the Trust and related games are significantly different from the strategies that derive from subgame perfect Nash equilibrium principles. We show that, for individual subjects, there is considerable flexibility of choice and belief across similar tasks and that the relationship between belief and choice is similarly flexible. To improve our ability to predict human behavior, we must take account of the flexible nature of human belief and choice

This paper considers the problem of patrolling multiple targets in a Euclidean environment by a single patrolling unit. We use game-theoretic approach and model the problem as a two-player zero-sum game in the extensive form. Based on the existing work in the domain of patrolling we propose a novel mathematical non-linear program for finding strategies in a discretized problem, in which we introduce a general concept of internal states of the patroller. We experimentally evaluate game value for the patroller for various graphs and strategy representations. The results suggest that adding internal states for the patroller yields better results in comparison to adding choice nodes in the used discretization.

The fragmentation of research in AI and robotics has created a vast repertoire of skills a robot could be equipped with but that must be manually integrated to form a complex action. We propose a novel evolutionary algorithm that aims at autonomously integrating, adapting and creating new actions by re-using skills that are either externally provided or previously generated. Complex actions are created by instantiating a Finite State Automaton and new skills are created using fully recurrent neural networks. We validated our approach in two scenarios, i.e. exploration and moving to pre-grasp positions. Our experiments show that complex actions can be created by composing independently developed skills. The results have been applied and tested with a real robot in a variety of scenarios.

Traditional social science research has been unable to satisfactorily aggregate individual level data to group, organization and systems levels, making it one of social science’s biggest challenges (Giles, 2011). For game and social theory, we believe that the fault can be attributed to the lack of valid distance measures (e.g., the arbitrary ordering of cooperation and competition precludes a Hilbert space distance metric for the ordering of and gradations between these social behaviors, making game theory normative). Alternatively, we offer a theory of social interdependence with countable mathematics based on bistable or multi-stable perspectives and linear algebra. The evidence that is available is supportive. It indicates that meaning is a one-sided, stable, classical interpretation, not only making the correspondence between beliefs and objective reality in social settings incomplete, raising questioning about static theories from earlier eras (i.e., Axelrod’s evolution of cooperation; Simon’s bounded rationality). The result indicates for open systems (democracies) that interpretations evolve naturally to become orthogonal (Nash equilibria), that orthogonal interpretations generate the information to drive social evolution, but that in closed systems (dictatorships), dependent on the enforcement of social cooperation and the suppression of opposing points of view, evolution slows or stops (e.g., China, Iran or Cuba), causing capital and energy to be wasted, misdirected or misallocated as leaders suppress the interpretations that they alone have the authority to label as unethical, immoral, or irreligious. We conclude that a mathematics based on NE is feasible.

Nexus Network Learner is an intelligent agent based simulation used to study Irregular Warfare (IW) in several major studies at the Department of Defense (DoD). Heterogeneous autonomous agents, each with their own separated inductive learning mechanism, have initial attributes and behaviors in proportion to demographic groups in the simulated population, and learn new behaviors as they serve culturally based goals. Nexus agents create a dynamic role-based network, and learn how to choose partners as well as what behaviors they should have with their network partners. As Nexus agents coevolve, nexus models the emergence of social institutions from individual behaviors, the fundamental social aggregation challenge. Nexus models the formation of learned vicious and virtuous cycles of behavior, some of which have higher average utility for the agents than others, and can be used to test the effects of interventions on the natural motivation-based system. An experiment is presented that uses Nexus to model the vicious cycle of corruption in an African country, from the first Irregular Warfare Analytical baseline at the Office of the Secretary of Defense (Messer 2009).