Browse Proceedings

Intelligent Tutoring Systems (ITS) is the interdisciplinary field that investigates how to devise educational systems that provide instruction tailored to the needs of individual learners, as many good teachers do. Research in this field has ...

Ecosystem Informatics brings together mathematical and computational tools to address scientific and policy challenges in the ecosystem sciences. These challenges include novel sensors for collecting data, algorithms for automated data cleaning, ...

Open-ended language communication remains an enormous challenge for autonomous robots. This paper argues that the notion of a language strategy is the appropriate vehicle for addressing this challenge. A language strategy packages all the procedures ...

Sensors provide computer systems with a window to the outside world. Activity recognition "sees" what is in the window to predict the locations, trajectories, actions, goals and plans of humans and objects. Building an activity recognition system ...

A tournament is a binary dominance relation on a set of alternatives. Tournaments arise in many contexts that are relevant to AI, most notably in voting (as a method to aggregate the preferences of agents). There are many works that deal with choice ...

We propose a novel method for helping humans make good decisions in complex games, for which common equilibrium solutions may be too difficult to compute or not relevant. Our method leverages and augments humans' natural use of arguments in the decision ...

We consider the problem of tactical assault planning in real-time strategy games where a team of friendly agents must launch an assault on an enemy. This problem offers many challenges including a highly dynamic and uncertain environment, multiple ...

Mechanisms (especially on the Internet) have begun allowing people or organizations to express richer preferences in order to provide for greater levels of overall satisfaction. In this paper, we develop an operational methodology for quantifying the ...

The POSSIBLE WINNER problem asks whether some distinguished candidate may become the winner of an election when the given incomplete votes are extended into complete ones in a favorable way. POSSIBLE WINNER is NP-complete for common voting rules such as ...

We study pursuit-evasion problems where a number of pursuers have to clear a given graph. We study when polynomial-time algorithms exist to determine how many pursuers are needed to clear a given graph and how a given number of pursuers should move on ...

While there are several languages for representing combinatorial preferences over sets of alternatives, none of these are well-suited to the representation of ordinal preferences over sets of goods (which are typically required to be monotonic). We ...

We introduce planning games, a study of interactions of self-motivated agents in automated planning settings. Planning games extend STRIPS-like models of single-agent planning to systems of multiple self-interested agents, providing a rich class of ...

We present and analyze coalitional affinity games, a family of hedonic games that explicitly model the value that an agent receives from being associated with other agents. We provide a characterization of the social-welfare maximizing coalition ...

We introduce coalitional games with beliefs (CGBs), a natural generalization of coalitional games to environments where agents possess private beliefs regarding the capabilities (or types) of others. We put forward a model to capture such agent-type ...

Runtime commitment verification is an important, open issue in multiagent research. To address it, we build on Yolum and Singh's formalization of commitment operations, on Chittaro and Montanari's cached event calculus, and on the SCIFF abductive logic ...

In many practical contexts where a number of agents have to find a common decision, the votes do not come all together at the same time. In such situations, we may want to preprocess the information given by the subelectorate (consisting of the voters ...

Voting on multiple related issues is an important and difficult problem. The key difficulty is that the number of alternatives is exponential in the number of issues, and hence it is infeasible for the agents to rank all the alternatives. A simple ...

In social choice, a preference function (PF) takes a set of votes (linear orders over a set of alternatives) as input, and produces one or more rankings (also linear orders over the alternatives) as output. Such functions have many applications, for ...

Graphical games provide compact representation of a multiagent interaction when agents' payoffs depend only on actions of agents in their local neighborhood. We formally describe the problem of learning a graphical game model from limited observation of ...

Voting theory can provide useful insights for multiagent preference aggregation. However, the standard setting assumes voters with preferences that are total orders, as well as a ballot language that coincides with the preference language. In typical AI ...

In 1992, Bartholdi, Tovey, and Trick [1992] opened the study of control attacks on elections--attempts to improve the election outcome by such actions as adding/deleting candidates or voters. That work has led to many results on how algorithms can be ...

Mixed multi-unit combinatorial auctions (MMUCAs) are extensions of classical combinatorial auctions (CAs) where bidders trade transformations of goods rather than just sets of goods. Solving MMUCAs, i.e., determining the sequences of bids to be accepted ...

Computing a Nash equilibrium in multiplayer stochastic games is a notoriously difficult problem. Prior algorithms have been proven to converge in extremely limited settings and have only been tested on small problems. In contrast, we recently presented ...

A significantly complete account of the complexity underlying the computation of relevant solution concepts in compact coalitional games is provided. The starting investigation point is the setting of graph games, about which various long-standing open ...

For some well-known games, such as the Traveler's Dilemma or the Centipede Game, traditional game-theoretic solution concepts--most notably Nash equilibrium--predict outcomes that are not consistent with empirical observations. We introduce a new ...

We study a multi-step hider-seeker game where the hider is moving on a graph and, in each step, the seeker is able to search c subsets of the graph nodes. We model this game as a zero-sum Bayesian game, which can be solved in weakly polynomial time in ...

This paper considers the setting wherein a group of agents (e.g., robots) is seeking to obtain a given tangible good, potentially available at different locations in a physical environment. Traveling between locations, as well as acquiring the good at ...

In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilistic reasoning. Our specific focus is a class of oversubscribed scheduling problems where the goal is to ...

Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world ...