Let us explain our motivations for studying the logic of fuzzy belief and common belief. It is not so unusual that one We formalize reasoning about fuzzy belief and believes something to some degree, or the degree of one's fuzzy common belief, especially incomparable beliefs, belief may be neither 0 nor 1. The notion of fuzzy belief is in multi-agent systems by using a logical system appropriate in such a case. Moreover, the notion of fuzzy based on Fitting's many-valued modal logic, common belief can be appropriate even in a case where any where incomparable beliefs mean beliefs whose degrees agent of a group does not have a fuzzy belief. To see this, are not totally ordered. Completeness and consider the following question. Is there anything that all the decidability results for the logic of fuzzy belief people in the world believe? Strictly speaking, there may be and common belief are established while implicitly no such thing as a common belief among all the people in the exploiting the duality-theoretic perspective on Fitting's world. Even if so, there may be something that most of the logic that builds upon the author's previous people in the world believe.

Grammatical agreement is present in many of the world's languages today and has become an essential feature that guides linguistic processing. When two words in a sentence are said to "agree", this means that they share certain features such as "gender", "number", "person" or others. The primary hypothesis of this paper is that marking agreement within one linguistic phrase reduces processing effort as phrasal constituents can more easily be recognized. The drive to reduce processing effort introduces the rise of agreement marking in a population of multiple agents by means of an incrementally aligned mapping between the most discriminatory features of a particular linguistic unit and their associative markers. A series of experiments compare feature selection methods for one-to-one agreement mappings, and show how an agreement system can be bootstrapped.

Many collective decision making problems have a combinatorial structure: the agents involved must decide on multiple issues and their preferences over one issue may depend on the choices adopted for some of the others. Voting is an attractive method for making collective decisions, but conducting a multi-issue election is challenging. On the one hand, requiring agents to vote by expressing their preferences over all combinations of issues is computationally infeasible; on the other, decomposing the problem into several elections on smaller sets of issues can lead to paradoxical outcomes. Any pragmatic method for running a multi-issue election will have to balance these two concerns. We identify and analyse the problem of generating an agenda for a given election, specifying which issues to vote on together in local elections and in which order to schedule those local elections.

Imitation learning refers to the problem of learning how to behave by observinga teacher in action. We consider imitation learning in relational domains, in which there is a varying number of objects and relations among them. In prior work, simple relational policies are learned by viewing imitation learning as supervised learning of a function from states to actions. For propositional worlds, functional gradient methods have been proved to be beneficial. They are simpler to implement than most existing methods, more efficient, more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the form of the function. Building on recent generalizations of functional gradient boosting to relational representations, we implement a functional gradient boosting approach to imitation learning in relational domains. In particular, given a set of traces from the human teacher, our system learns a policy in the form of a set of relational regression trees that additively approximate the functional gradients. The use of multiple additive trees combined with relational representation allows for learning more expressive policies than what has been done before. We demonstrate the usefulness of our approach in several different domains.

Differences in social responses of individuals can often be related to differences in functioning of neurological mechanisms. This paper presents a cognitive agent model capable of showing different types of social response patterns based on such mechanisms, adopted from theories on mirror neuron systems, emotion regulation, empathy, and autism spectrum disorders. The presented agent model provides a basis for human-like social response patterns of virtual agents in the context of simulation-based training (e.g., for training of therapists), gaming, or for agent-based generation of virtual stories.

We investigate the emergence and stability of social conventions for efficiently resolving conflicts through reinforcement learning. Facilitation of coordination and conflict resolution is an important issue in multi-agent systems. However, exhibiting coordinated and negotiation activities is computationally expensive. In this paper, we first describe a conflict situation using a Markov game which is iterated if the agents fail to resolve their conflicts, where the repeated failures result in an inefficient society. Using this game, we show that social conventions for resolving conflicts emerge, but their stability and social efficiency depend on the payoff matrices that characterize the agents. We also examine how unbalanced populations and small heterogeneous agents affect efficiency and stability of the resulting conventions. Our results show that (a) a type of indecisive agent that is generous for adverse results leads to unstable societies, and (b) selfish agents that have an explicit order of benefits make societies stable and efficient.

We define an extension of stit logic that encompasses subjective probabilities representing beliefs about simultaneous choice exertion of other agents. The formalism enables us to express the notion of "attempt" as a choice exertion that maximizes the chance of success with respect to an action effect. The notion of attempt (or effort) is central in philosophical and legal discussions on responsibility and liability.

We present a novel methodology for decision-making by computer agents that leverages a computational concept of emotions. It is believed that emotions help living organisms perform well in complex environments. Can we use them to improve the decision-making performance of computer agents? We explore this possibility by formulating emotions as mathematical operators that serve to update the relative priorities of the agent's goals. The agent uses rudimentary domain knowledge to monitor the expectation that its goals are going to be accomplished in the future, and reacts to changes in this expectation by "experiencing emotions." The end result is a projection of the agent's long-run utility function, which might be too complex to optimize or even represent, to a time-varying valuation function that is being myopically maximized by selecting appropriate actions. Our methodology provides a systematic way to incorporate emotion into a decision-theoretic framework, and also provides a principled, domain-independent methodology for generating heuristics in novel situations. We test our agents in simulation in two domains: restless bandits and a simple foraging environment. Our results indicate that emotion-based agents outperform other reasonable heuristics for such difficult domains, and closely approach computationally expensive near-optimal solutions, whenever these are computable, yet requiring only a fraction of the cost.

Problems that require multiple agents to follow non-interfering paths from their current states to their respective goal states are called cooperative pathfinding problems. We present the first {complete algorithm for finding these paths that is sufficiently fast for real-time applications. Furthermore, our algorithm offers a trade-off between running time and solution quality. We then refine our algorithm into an anytime algorithm that first quickly finds a solution, and then uses any remaining time to incrementally improve that solution until it is optimal or the algorithm is terminated. We compare our algorithms to those in the literature and show that in addition to completeness, our algorithms offer improved solution quality as well as competitive running time.

Forming effective coalitions is a major research challenge in AI and multi-agent systems (MAS). Thus, coalitional games, including Coalition Structure Generation (CSG), have been attracting considerable attention from the AI research community. Traditionally, the input of a coalitional game is a black-box function called a characteristic function. A range of previous studies have found that many problems in coalitional games tend to be computationally intractable when the input is a black-box function. Recently, several concise representation schemes for a characteristic function have been proposed. Although these schemes are effective for reducing the representation size, most problems remain computationally intractable. In this paper, we develop a new concise representation scheme based on the idea of agent types. Intuitively, a type represents a set of agents, which are recognized as having the same contribution. This representation can be exponentially more concise than existing concise representation schemes. Furthermore, this idea can be used in conjunction with existing schemes to further reduce the representation size. Moreover, we show that most of the problems in coalitional games, including CSG, can be solved in polynomial time in the number of agents, assuming the number of possible types is fixed.