We propose a novel online planning algorithm for ad hoc team settings — challenging situations in which an agent must collaborate with unknown teammates without prior coordination. Our approach is based on constructing and solving a series of stage games, and then using biased adaptive play to choose actions. The utility function in each stage game is estimated via Monte-Carlo tree search using the UCT algorithm. We establish analytically the convergence of the algorithm and show that it performs well in a variety of ad hoc team domains.

In multi-issue automated negotiation against unknown opponents, a key part of effective negotiation is the choice of concession strategy. In this paper, we develop a principled concession strategy, based on Gaussian processes predicting the opponent's future behaviour. We then use this to set the agent's concession rate dynamically during a single negotiation session. We analyse the performance of our strategy and show that it outperforms the state-of-the-art negotiating agents from the 2010 Automated Negotiating Agents Competition, in both a tournament setting and in self-play, across a variety of negotiation domains.

Note that this extra to make decisions about which plans are used to information is included as a preference rather than a goal, achieve their goals. Usually the choice of which as it is acceptable to satisfy the goal without satisfying the plan to use to achieve a particular goal is left up preference. For example, if the user prefers to fly on Dodgy to the system to determine. In this paper we show Airlines, but no such flights are available, then specifying this how preferences, which can be set by the user of the as a preference means that the user can still have a holiday; system, can be incorporated into the BDI execution specifying this as a goal would mean that the user refuses to process and used to guide the choices made.

We present the notion of Social Instruments as mechanisms that facilitate the emergence of conventions from repeated interactions between members of a society. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions when effectively addressing coordination and learning problems, paying special attention to dissolving meta-stable subconventions. Initial experiments throw some light on how Self-Reinforcing Substructures (SRS) in the network prevent full convergence, resulting in reduced convergence rates. The use of an effective composed social instrument (observation + rewiring) allow agents to eliminate the subconventions that otherwise remained meta-stable.

Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experi- ences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed mem- bership to categories; (2) learn a series of emer- gent relations between trustees observable proper- ties and their effective abilities to fulfill tasks in sit- uated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of ar- chitectures combining categorization abilities, indi- vidual experiences and context awareness are eval- uated and compared in simulated experiments.

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.

Envy-freeness is a well-known fairness concept for analyzing mechanisms. Its traditional definition requires that no individual envies another individual. However, an individual (or a group of agents) may envy another group, even if she (or they) does not envy another individual. In mechanisms with monetary transfer, such as combinatorial auctions, considering such fairness requirements, which are refinements of traditional envy-freeness, is meaningful and brings up a new interesting research direction in mechanism design. In this paper, we introduce two new concepts of fairness called envy-freeness of an individual toward a group, and envy-freeness of a group toward a group. They are natural extensions of traditional envy-freeness. We discuss combinatorial auction mechanisms that satisfy these concepts. First, we characterize such mechanisms by focusing on their allocation rules. Then we clarify the connections between these concepts and three other properties: the core, strategy-proofness, and false-name-proofness.

The VCG mechanism is the gold standard for combinatorial auctions (CAs), and it maximizes social welfare. In contrast, the revenue-maximizing (aka optimal) CA is unknown, and designing one is NP-hard. Therefore, research on optimal CAs has progressed into special settings. Notably, Levin [1997] derived the optimal CA for complements when each agent's private type is one-dimensional. We introduce a new research avenue for increasing revenue where we poke holes in the allocation space — based on the bids — and then use a welfare-maximizing allocation rule within the remaining allocation set. In this paper, the first step down this avenue, we introduce a new form of "reserve pricing" into CAs. We show that Levin's optimal revenue can be 2-approximated by using "monopoly reserve prices" to curtail the allocation set, followed by welfare-maximizing allocation and Levin's payment rule. A key lemma of potential independent interest is that the expected revenue from any truthful allocation-monotonic mechanism equals the expected virtual valuation; this generalizes Myerson's lemma [1981] from the single-parameter environment. Our mechanism is close to the gold standard and thus easier to adopt than Levin's. It also requires less information about the prior over the bidders' types, and is always more efficient. Finally, we show that the optimal revenue can be 6-approximated even if the "reserve pricing" is required to be symmetric across bidders.

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.

One approach for artificially intelligent agents wishing to maximise some performance metric in a given domain is to learn from a collection of training data that consists of actions or decisions made by some expert, in an attempt to imitate that expert's style. We refer to this type of agent as an expert imitator. In this paper we investigate whether performance can be improved by combining decisions from multiple expert imitators. In particular, we investigate two existing approaches for combining decisions. The first approach combines decisions by employing ensemble voting between multiple expert imitators. The second approach dynamically selects the best imitator to use at runtime given the performance of the imitators in the current environment. We investigate these approaches in the domain of computer poker. In particular, we create expert imitators for limit and no limit Texas Hold'em and determine whether their performance can be improved by combining their decisions using the two approaches listed above.