As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed computation so that sensitive data can be supplied and processed in encrypted form, and only the final result is made known. In this paper, we examine how such a paradigm can be used to implement constraint satisfaction, a technique that can solve a broad class of AI problems such as resource allocation, planning, scheduling, and diagnosis. Most previous work on privacy in constraint satisfaction only attempted to protect specific types of information, in particular the feasibility of particular combinations of decisions. We formalize and extend these restricted notions of privacy by introducing four types of private information, including the feasibility of decisions and the final decisions made, but also the identities of the participants and the topology of the problem. We present distributed algorithms that allow computing solutions to constraint satisfaction problems while maintaining these four types of privacy. We formally prove the privacy properties of these algorithms, and show experiments that compare their respective performance on benchmark problems.

In this paper, a new structure of cooperative learning automata so-called extended learning automata (eDLA) is introduced. Based on the proposed structure, a new iterative randomized heuristic algorithm for finding optimal sub-graph in a stochastic edge-weighted graph through sampling is proposed. It has been shown that the proposed algorithm based on new networked-structure can be to solve the optimization problems on stochastic graph through less number of sampling in compare to standard sampling. Stochastic graphs are graphs in which the edges have an unknown distribution probability weights. Proposed algorithm uses an eDLA to find a policy that leads to an induced sub-graph that satisfies some restrictions such as minimum or maximum weight (length). At each stage of the proposed algorithm, eDLA determines which edges to be sampled. This eDLA-based proposed sampling method may result in decreasing unnecessary samples and hence decreasing the time that algorithm requires for finding the optimal sub-graph. It has been shown that proposed method converge to optimal solution, furthermore the probability of this convergence can be made arbitrarily close to 1 by using a sufficiently small learning rate. A new variance-aware threshold value was proposed that can be improving significantly convergence rate of the proposed eDLA-based algorithm. It has been shown that the proposed algorithm is competitive in terms of the quality of the solution

Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make a sub-swarm, are evolved by the particle swarm optimization and clonal selection algorithm. Experimental results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its popular methods.

This research proposes the use of imitation based learning to build collaborative strategies for a team of agents. Imitation based learning involves learning from an expert by observing her demonstrating a task and then replicating it. This mechanism makes it extremely easy for a knowledge engineer to transfer knowledge to a software agent via human demonstrations. This research aims to apply imitation to learn not only the strategy of an individual agent but also the collaborative strategy of a team of agents to achieve a common goal. The effectiveness of the proposed methodology is being assessed in the domain of RoboCup Soccer Simulation 3D which is a promising platform to address many of the complex real-world problems and offers a truly dynamic, stochastic, and partially-observable environment.

In reputation systems for multiagent-based e-marketplaces, buying agents model the reputation of selling agents based on ratings shared by other buyers (called advisors). With the existence of unfair rating attacks from dishonest advisors, the effectiveness of reputation systems thus heavily relies on whether buyers can accurately determine which advisors to include in trust networks and their trustworthiness. In this paper, we propose two approaches to deal with unfair rating attacks. The first method is to combine the advantages of different categorical trust models. Secondly, we propose a novel multiagent evolutionary trust model (MET) where each buyer constructs its trust network (information about which advisors should be include in the network and their trustworthiness) by the evolutionary model. Experimental results demonstrate the proposed algorithms are more robust than the state-of-the-art trust models against various unfair rating attacks.

In this paper, we state the challenges of high-level program execution in multi-agent settings. We first introduce high-level program execution and the related work. Then we describe the completed work, the future work and its approaches. We conclude with the expected contributions of our research.

Our research is within the subfield of modeling trust and reputation in multi-agent systems for online communities. Specifically, in an online community involving users and entities, users provide opinions (ratings) to entities. For each user, we are interested in addressing two problems: (1) how to accurately model the reputation of entities by aggregating opinions from all the users (advisors); and (2) how to cope with the dishonesty of an advisor in providing opinions as well as her subjectivity difference with the user.

Social norms are one of the mechanisms for decentralized societies to achieve coordination amongst individuals. Such norms are conflict resolution strategies that develop from the population interactions instead of a centralized entity dictating agent protocol.One of the most important characteristics of social norms is that they are imposed by the members of the society, and they are responsible for the fulfillment and defense of these norms. By allowing agents to manage (impose, abide by and defend) social norms, societies achieve a higher degree of freedom by lacking the necessity of authorities supervising all the interactions amongst agents. In this article we summarize the contributions of my dissertation, where we provide an unifying framework for the analysis of social norms in virtual societies, providing an strong emphasis on virtual agents and humans.

In human conversation, meaning is transported through several channels such as verbal and nonverbal behavior. Certain of these behavioral aspects are culturally dependent. Mutual understanding or acceptance is thus, amongst others, depended on the cultural background of the interlocutors. When designing virtual character behavior, culture should be considered as it may improve the character's acceptance by users of certain cultural backgrounds. This paper proposes a hybrid approach for the generation of culture-specific behaviors in a multiagent system. A computational model has been established by refining theoretical knowledge of culture-specific behavior with statistical data extracted from a video corpus of German and Japanese first-time meetings. Evaluation studies of such culturally enhanced virtual characters were conducted in both targeted cultures. Results indicate that human observers tend to prefer character behavior that was designed to resemble their own cultural background.

Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social causality and responsibility judgment according to an agent’s causal knowledge and observations of interaction. The proposed model is also empirically validated via experimental study.