We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the agents' operational know-how, as defined by their libraries of abstract plans. Inspired by ATLES, a variant itself of ATL, it is possible in our logic to explicitly refer to "rational" strategies for agents developed under the Belief-Desire-Intention agent programming paradigm. This allows us to express and verify properties of BDI systems using ATL-type logical frameworks.

In this paper, the early design of our self-organized agent-based simulation model for exploration of synaptic connections that faithfully generates what is observed in natural situation is given. While we take inspiration from neuroscience, our intent is not to create a veridical model of processes in neurodevelopmental biology, nor to represent a real biological system. Instead, our goal is to design a simulation model that learns acting in the same way of human nervous system by using findings on human subjects using reflex methodologies in order to estimate unknown connections.

The behavior composition problem involves automatically building a controller that is able to realize a desired, but unavailable, target system (e.g., a house surveillance) by suitably coordinating a set of available components (e.g., video cameras, blinds, lamps, a vacuum cleaner, phones, etc.) Previous work has almost exclusively aimed at bringing about the desired component in its totality, which is highly unsatisfactory for unsolvable problems. In this work, we develop an approach for approximate behavior composition without departing from the classical setting, thus making the problem applicable to a much wider range of cases. Based on the notion of simulation, we characterize what a maximal controller and the "closest" implementable target module (optimal approximation) are, and show how these can be computed using ATL model checking technology for a special case. We show the uniqueness of optimal approximations, and prove their soundness and completeness with respect to their imported controllers.

Over the years, competitions have been important catalysts for progress in artificial intelligence. We describe the goal of the overall Trading Agent Competition and highlight particular competitions. We discuss its significance in the context of today's global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past ten years. Since its introduction in 2000, TAC has attracted more than 350 entries and brought together researchers from AI and beyond.

We then describe the process of getting to deployment, covering Task learning provides tremendous value for technical challenges encountered, unit CPOF by enabling individual users and collective engagement activities, and an Army-led assessment command staffs to create customized, automated of the technology. Next, we discuss the fielding information-management schemes tailored to of the technology, including tradeoffs made to individual preferences and the staff's standard ensure deployability, the impact of the deployed operating procedures, without needing software technology, and lessons learned. We close with a engineers for extensive recoding. Task learning can summary of ongoing work to deploy additional reduce work load and stress, can enable managing functionality and to broaden the user base for task more tasks with better effectiveness, and can facilitate learning in CPOF.

In many real-life domains, such as trading environments, selfinterested entities need to operate subject to limited time and information. Additionally, the web has mediated an ever broader range of transactions, urging participants to concurrently trade across multiple markets. All these have generated the need for technologies that empower prompt investigation of large volumes of data and rapid evaluation of numerous alternative strategies in the face of constantly changing market conditions (Bichler, Gupta, and Ketter 2010). AI and machine-learning techniques, including neural networks and genetic algorithms, are continuously gaining ground in the support of such trading scenarios. User modeling, price forecasting, market equilibrium prediction, and strategy optimization are typical cases where AI typically provides reliable solutions. Yet, the adoption and deployment of AI practices in real trading environments remains limited, since the proprietary nature of markets precludes open benchmarking, which is critical for further scientific progress.

Consider a multi-agent system in a dynamic and uncertain environment. Each agent's local decision problem is modeled as a Markov decision process (MDP) and agents must coordinate on a joint action in each period, which provides a reward to each agent and causes local state transitions. A social planner knows the model of every agent's MDP and wants to implement the optimal joint policy, but agents are self-interested and have private local state. We provide an incentive-compatible mechanism for eliciting state information that achieves the optimal joint plan in a Markov perfect equilibrium of the induced stochastic game. In the special case in which local problems are Markov chains and agents compete to take a single action in each period, we leverage Gittins allocation indices to provide an efficient factored algorithm and distribute computation of the optimal policy among the agents. Distributed, optimal coordinated learning in a multi-agent variant of the multi-armed bandit problem is obtained as a special case.

Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in solving decentralized POMDPs with large horizons. We generalize the algorithm and improve its scalability by reducing the complexity with respect to the number of observations from exponential to polynomial. We derive error bounds on solution quality with respect to this new approximation and analyze the convergence behavior. To evaluate the effectiveness of the improvements, we introduce a new, larger benchmark problem. Experimental results show that despite the high complexity of decentralized POMDPs, scalable solution techniques such as MBDP perform surprisingly well.

We present a memory-bounded optimization approach for solving infinite-horizon decentralized POMDPs. Policies for each agent are represented by stochastic finite state controllers. We formulate the problem of optimizing these policies as a nonlinear program, leveraging powerful existing nonlinear optimization techniques for solving the problem. While existing solvers only guarantee locally optimal solutions, we show that our formulation produces higher quality controllers than the state-of-the-art approach. We also incorporate a shared source of randomness in the form of a correlation device to further increase solution quality with only a limited increase in space and time. Our experimental results show that nonlinear optimization can be used to provide high quality, concise solutions to decentralized decision problems under uncertainty.

We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) representation. If the decision of an agent participates in no reasoning patterns, then we can effectively ignore that decision for the purpose of calculating a Nash equilibrium for the game. In some cases, this can lead to exponential time savings in the process of equilibrium calculation. Moreover, our algorithm can be used to enumerate the reasoning patterns in a game, which can be useful for constructing more effective computerized agents interacting with humans.