A general game player automatically learns to play arbitrary new games solely by being told their rules. For this purpose games are specified in the game description language GDL, a variant of Datalog with function symbols and a few known keywords. In its latest version GDL allows to describe nondeterministic games with any number of players who may have imperfect, asymmetric information. We analyse the epistemic structure and expressiveness of this language in terms of epistemic modal logic and present two main results: The operational semantics of GDL entails that the situation at any stage of a game can be characterised by a multi-agent epistemic (i.e., S5-) model; (2) GDL is sufficiently expressive to model any situation that can be described by a (finite) multi-agent epistemic model.

MAPP has been previously shown as a state-of-the-art multi-agent path planning algorithm on criteria including scalability and success ratio (i.e., percentage of solved units) on realistic game maps. MAPP further provides a formal characterization of problems it can solve, and low-polynomial upper bounds on the resources required. However, until now, MAPP's solution quality had not been extensively analyzed. In this work we empirically analyze the quality of MAPP's solutions, using multiple quality criteria such as the total travel distance, the makespan and the sum of actions (including move and wait actions). We also introduce enhancements that improve MAPP's solution quality significantly. For example, the sum of actions is cut to half on average. The improved MAPP is competitive in terms of solution quality with FAR and WHCA*, two successful algorithms from the literature, and maintains its advantages on different performance criteria, such as scalability, success ratio, and ability to tell apriori if it will succeed in the instance at hand. As optimal algorithms have limited scalability, evaluating the quality of the solutions provided by suboptimal algorithms is another important topic. Using lower bounds of optimal values, we show that MAPP's solutions have a reasonable quality. For example, MAPP's total travel distance is on average 19% longer than a lower bound on the optimal value.

Plants can have positive effects on each other in numerous ways, including protection from harsh environmental conditions. This phenomenon, known as facilitation, occurs in water-stressed environments when shade from larger shrubs protects smaller annuals from harsh sun, enabling them to exist on scarce water. The topic of this paper is a model of this phenomenon that allows search algorithms to find residential landscape designs that incorporate facilitation to conserve water. This model is based in botany; it captures the growth requirements of real plant species in a fitness function, but also includes a penalty term in that function that encourages facilitative interactions with other plants on the landscape. To evaluate the effectiveness of this approach, two search strategies--simulated annealing and agent-based search--were applied to models of different collections of simulated plant types and landscapes with different light distributions. These two search strategies produced landscape designs with different spatial distributions of the larger plants. All designs exhibited facilitation and lower water use than designs where facilitation was not included.

One proposed approach to managing a large complex Smart Grid is through Broker Agents who buy electrical power from distributed producers, and also sell power to consumers, via a Tariff Market--a new market mechanism where Broker Agents publish concurrent bid and ask prices. A key challenge is the specification of the market strategy that the Broker Agents should use in order to earn profits while maintaining the market's balance of supply and demand. Interestingly, previous work has shown that a Broker Agent can learn its strategy, using Markov Decision Processes (MDPs) and Q-learning, and outperform other Broker Agents that use predetermined or randomized strategies. In this work, we investigate the more representative scenario in which multiple Broker Agents, instead of a single one, are independently learning their strategies. Using a simulation environment based on real data, we find that Broker Agents who employ periodic increases in exploration achieve higher rewards. We also find that varying levels of market dominance in customer allocation models result in remarkably distinct outcomes in market prices and aggregate Broker Agent rewards. The latter set of results can be explained by established economic principles regarding the emergence of monopolies in market-based competition, further validating our approach.

We consider settings in which a semi-autonomous agent has uncertain knowledge about its environment, but can ask what action the human operator would prefer taking in the current or in a potential future state. Asking queries can improve behavior, but if queries come at a cost (e.g., due to limited operator attention), the value of each query should be maximized. We compare two strategies for selecting action queries: 1) based on myopically maximizing expected gain in long-term value, and 2) based on myopically minimizing uncertainty in the agent's policy representation. We show empirically that the first strategy tends to select more valuable queries, and that a hybrid method can outperform either method alone in settings with limited computation.

The hypothesis is presented that "cognitive synergy" -- proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory -- may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.

In many multi-agent applications such as distributed sensor nets, a network of agents act collaboratively under uncertainty and local interactions. Networked Distributed POMDP (ND-POMDP) provides a framework to model such cooperative multi-agent decision making. Existing work on ND-POMDPs has focused on offline techniques that require accurate models, which are usually costly to obtain in practice. This paper presents a model-free, scalable learning approach that synthesizes multi-agent reinforcement learning (MARL) and distributed constraint optimization (DCOP). By exploiting structured interaction in ND-POMDPs, our approach distributes the learning of the joint policy and employs DCOP techniques to coordinate distributed learning to ensure the global learning performance. Our approach can learn a globally optimal policy for ND-POMDPs with a property called groupwise observability. Experimental results show that, with communication during learning and execution, our approach significantly outperforms the nearly-optimal non-communication policies computed offline.

We consider the problem of designing auctions for settings in Theorem 1 (Dominant strategy impossibility (Larson which bidders have to pay a cost to learn about their preferences, and Sandholm 2004a)). There does not exist any mechanism and hence can face tradeoffs between the cost and accuracy that is strategic deliberation-proof, strategy-dependent, of their preference information. Such bidders are called non-misleading, and preference-formation independent in deliberative agents, and have featured in a wide variety of dominant-strategy equilibrium across all possible quasilinear auction models. For example, costly deliberation can model deliberative-agent settings.

Without commitment, this game is solvable by iterated Game theory provides a mathematical framework for rational strict dominance: U strictly dominates D for player 1; after action in settings with multiple agents. As such, algorithms removing D, L strictly dominates R for player 2. So for computing game-theoretic solutions are of great the iterated strict dominance outcome (and hence the only interest to the multiagent systems community in AI. equilibrium outcome) is (U, L), resulting in a utility of 1 for It has long been well known in game theory that being player 1. However, if player 1 can commit to a pure strategy able to commit to a course of action before the before player 2 moves, then player 1 is better off committing other player(s) move(s)--often referred to as a Stackelberg to D, thereby incentivizing player 2 to play R, resulting model (von Stackelberg 1934)--can bestow significant in a utility of 2 for player 1. Even better for player 1 is to advantages. In recent years, the problem of computing commit to a mixed strategy of (.49U,.51D); this still incentivizes an optimal strategy to commit to has started to receive player 2 to play R and results in an expected utility a significant amount of attention, especially in the multiagent of.49

Equilibrium computation in games is currently considered one of the most challenging issues in AI. In this paper, we provide, to the best of our knowledge, the first algorithm to compute a Selten's extensive-form perfect equilibrium (EFPE) with two--player games. EFPE refines the Nash equilibrium requiring the equilibrium to be robust to slight perturbations of both players' behavioral strategies. Our result puts the computation of an EFPE into the PPAD class, leaving open the question whether or not the problem is hard. Finally, we experimentally evaluate the computational time spent to find an EFPE and some relaxations of EFPE.