Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from the optimal value. This paper considers a variable-size FSC to represent the local policy of each agent. These variable-size FSCs are constructed using a stick-breaking prior, leading to a new framework called decentralized stick-breaking policy representation (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods.

Interactive dynamic influence diagrams(I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters(NPCs) in real-time strategy(RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.

The idea of only knowing is a natural and intuitive notion to precisely capture the beliefs of a knowledge base. However, an extension to the many agent case, as would be needed in many applications, has been shown to be far from straightforward. For example, previous Kripke frame-based accounts appeal to proof-theoretic constructions like canonical models, while more recent works in the area abandoned Kripke semantics entirely. We propose a new account based on Moss’ characteristic formulas, formulated for the usual Kripke semantics. This is shown to come with other benefits: the logic admits a group version of only knowing, and an operator for assessing the epistemic entrenchment of what an agent or a group only knows is definable. Finally, the multi-agent only knowing operator is shown to be expressible with the cover modality of classical modal logic, which then allows us to obtain a completeness result for a fragment of the logic.

To address We present a novel negotiation protocol to facilitate this challenge, Alam et al. [2013b] presented a protocol to energy exchange between off-grid homes that facilitate negotiation over energy exchange. Their protocol are equipped with renewable energy generation and restricts the type and number of offers such that negotiation electricity storage. Our protocol imposes restrictions leads to a subgame perfect Nash equilibrium (SPNE). However, over negotiation such that it reduces the complex their protocol only allows point-to-point communication interdependent multi-issue negotiation to one and relies on a fully connected network topology (i.e., where agents have a strategy profile in subgame each home is connected to all other homes in the community) perfect Nash equilibrium. We show that our protocol whereby the number of connections and messages exchanged; is concurrent, scalable and; under certain conditions; grow quadratically with the number of connected leads to Pareto-optimal outcomes.

We study a setting of non-atomic routing in a network of m parallel links with asymmetry of information. While a central entity (such as a GPS navigation system) — a mediator hereafter — knows the cost functions associated with the links, they are unknown to the individual agents controlling the flow. The mediator gives incentive compatible recommendations to agents, trying to minimize the total travel time. Can the mediator do better than when agents minimize their travel time selfishly without coercing agents to follow his recommendations? We study the mediation ratio: the ratio between the mediated equilibrium obtained from an incentive compatible mediation protocol and the social optimum. We find that mediation protocols can reduce the efficiency loss compared to the full revelation alternative, and compared to the non mediated Nash equilibrium. In particular, in the case of two links with affine cost functions, the mediation ratio is at most 8/7, and remains strictly smaller than the price of anarchy of 4/3 for any fixed m. Yet, it approaches the price of anarchy as m grows. For general (monotone) cost functions, the mediation ratio is at most m, a significant improvement over the unbounded price of anarchy

This paper considers the kinds of AI systems we want involved in art and art practice. We explore this relationship from three perspectives: as artists interested in expanding and developing our own creative practice; as AI researchers interested in building new AI systems that contribute to the understanding and development of art and art practice; and as audience members interested in experiencing art. We examine the nature of both art practice and experiencing art to ask how AI can contribute. To do so, we review the history of work in intelligent agents which broadly speaking sits in two camps: autonomous agents (systems that can exhibit intelligent behaviour independently) in one, and multi-agent systems (systems which interact with other systems in communities of agents) in the other. In this context we consider the nature of the relationship between AI and Art and introduce two opposing concepts: that of “Heroic AI”, to describe the situation where the software takes on the role of the lone creative hero and “Collaborative AI” where the system supports, challenges and provokes the creative activity of humans. We then set out what we believe are the main challenges for AI research in understanding its potential relationship to art and art practice.

This paper studies the complexity of model checking multiagent systems, in particular systems succinctly described by two practical representations: concurrent representation and symbolic representation. The logics we concern include branching time temporal logics and several variants of alternating time temporal logics.

Trust systems are widely used to facilitate interactions among agents based on trust evaluation. These systems may have robustness issues, that is, they are affected by various attacks. Designers of trust systems propose methods to defend against these attacks. However, they typically verify the robustness of their defense mechanisms (or trust models) only under specific attacks. This raises problems: first, the robustness of their models is not guaranteed as they do not consider all attacks. Second, the comparison between two trust models depends on the choice of specific attacks, introducing bias. We propose to quantify the strength of attacks, and to quantify the robustness of trust systems based on the strength of the attacks it can resist.Our quantification is based on information theory, and provides designers of trust systems a fair measurement of the robustness.

Such prediction is important to take remedy procedures to increase a team's performance. Existing Teams of voting agents have great potential in finding methods are tailored for specific domains [Ramos and optimal solutions. However, there are fundamental Ayanegui, 2008]. In Nagarajan et al. [2015] I introduce a challenges to effectively use such teams: (i) novel domain independent technique, which learns a prediction selecting agents; (ii) aggregating opinions; (iii) assessing function using only the voting patterns of a team.

This dissertation develops the intelligent-Coalition Formation framework for Humans and Robots (i-CiFHaR), an intelligent decision making frameworkfor multi-agent coalition formation. i-CiFHaR is a first of its kind that incorporates a library of coalition formation algorithms; employs unsupervised learning to mine crucial patterns among these algorithms; and leverages probabilistic reasoning to derive the most appropriate algorithm(s) to apply in accordance with multiple mission criteria. The dissertation also contributes to the state-of-the-art in swarm intelligence by addressing the search stagnation limitation of existing ant colony optimization algorithms (ACO) by integrating the simulated annealing mechanism. The experimental results demonstrate that the presented hybrid ACO algorithms significantly outperformed the best existing ACO approaches, when applied to three NP-complete optimization problems (e.g., traveling salesman problem, maximal clique problem, multi-agent coalition formation problem).