This year's conferences were held in Perhaps one of the true litmus tests of any conference is the caliber of the invited speakers. Sensibility: Sentiment Analysis, Opinion and research manager at Microsoft Research) The distinguished Robert S. Englemore Mining, and the Computational who gave his AAAI presidential Memorial Award Lecture was delivered Treatment of Subjective Language"), address, "Artificial Intelligence in the by Kenneth Ford (Florida Institute while Seth C. Goldstein (Carnegie Open World." Mel lon University) discussed revolutionary Chris Urmson (Carnegie Mellon In his lecture, "Toward Cognitive work in self-reconfiguring programmable University), a leading member of the Prostheses," Ford discussed human-centered matter composed of ensembles of submillimeter robots in his DARPA Urban Grand Challenge winning computing to amplify talk, "Realizing Claytronics: A Challenge team, described the race and winning human cognition and perception. Instead of the learning for network analysis in ("From Images to Scenes: Using popular competition, which has his talk, "Making Sense of Complex Lots of Data to Infer Geometric, Photometric, pushed the envelope of mobile robotics Networks." David Haussler (University and Semantic Scene Properties since its inception, this year was of California, Santa Cruz) traced the from a Single Image"), and Lillian host to a Robot Workshop and Exhibition.

SPARK'08 was the first edition of a workshop series designed to provide a stable, longterm forum where researchers could discuss Workshop (SPARK) was established to help address this issue. Building on precursory events, SPARK'08 was the first workshop designed Scheduling (ICAPS-08) held in Sydney, Australia, in September 2008. Like its immediate predecessor (the ICAPS'07 Workshop on Moving Planning and Scheduling Systems), the 2008 SPARK workshop was collocated with the International Conference on Automated Planning and Scheduling (ICAPS), a premier forum for research in AI planning and scheduling, and the International Conference on Principles and Practice of Constraint Programming (CP). A handful of outstanding application-oriented papers are presented each year at the ICAPS conference. Time and again, in invited talks and in open microphone discussion sessions such as ICAPS's Festivus (where conference participants air their grievances in an open and entertaining way), researchers have lamented the small number of applications papers accepted at conferences such as ICAPS, CP, and the AAAI Conference on Artificial Intelligence.

In this paper, we explore methods for comparing agent behavior with human behavior to assist with validation. Our exploration begins by considering a simple method of behavior comparison. Motivated by shortcomings in this initial approach, we introduce behavior bounding, an automated model-based approach for comparing behavior that is inspired, in part, by Mitchell's Version Spaces. We show that behavior bounding can be used to compactly represent both human and agent behavior. We argue that relatively low amounts of human effort are required to build, maintain, and use the data structures that underlie behavior bounding, and we provide a theoretical basis for these arguments using notions of PAC Learnability. Next, we show empirical results indicating that this approach is effective at identifying differences in certain types of behaviors and that it performs well when compared against our initial benchmark methods. Finally, we demonstrate that behavior bounding can produce information that allows developers to identify and fix problems in an agent's behavior much more efficiently than standard debugging techniques.

Cognitive radio is a breakthrough technology which is expected to have a profound impact on the way radio spectrum will be accessed, managed and shared in the future. In this paper I examine some of the implications of cognitive radio for future management of spectrum. Both a near-term view involving the opportunistic spectrum access model and a longer-term view involving a self-regulating dynamic spectrum access model within a society of cognitive radios are discussed.

Coordination of distributed agents is required for problems arising in many areas, including multi-robot systems, networking and e-commerce. As a formal framework for such problems, we use the decentralized partially observable Markov decision process (DEC-POMDP). Though much work has been done on optimal dynamic programming algorithms for the single-agent version of the problem, optimal algorithms for the multiagent case have been elusive. The main contribution of this paper is an optimal policy iteration algorithm for solving DEC-POMDPs. The algorithm uses stochastic finite-state controllers to represent policies. The solution can include a correlation device, which allows agents to correlate their actions without communicating. This approach alternates between expanding the controller and performing value-preserving transformations, which modify the controller without sacrificing value. We present two efficient value-preserving transformations: one can reduce the size of the controller and the other can improve its value while keeping the size fixed. Empirical results demonstrate the usefulness of value-preserving transformations in increasing value while keeping controller size to a minimum. To broaden the applicability of the approach, we also present a heuristic version of the policy iteration algorithm, which sacrifices convergence to optimality. This algorithm further reduces the size of the controllers at each step by assuming that probability distributions over the other agents' actions are known. While this assumption may not hold in general, it helps produce higher quality solutions in our test problems.

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability. We introduce the HAO* algorithm, a generalization of the AO* algorithm that performs search in a hybrid state space that is modeled using both discrete and continuous state variables, where the continuous variables represent monotonic resources. Like other heuristic search algorithms, HAO* leverages knowledge of the start state and an admissible heuristic to focus computational effort on those parts of the state space that could be reached from the start state by following an optimal policy. We show that this approach is especially effective when resource constraints limit how much of the state space is reachable. Experimental results demonstrate its effectiveness in the domain that motivates our research: automated planning for planetary exploration rovers.

We introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.

Many tasks in speech processing involve classification of long term characteristics of a speech segment such as language, speaker, dialect, or topic. A natural technique fordetermining these characteristics is to first convert the input speech into a sequence of tokens such as words, phones, etc. From these tokens, we can then look for distinctive sequences, keywords, that characterize the speech. In many applications, a set of distinctive keywords may not be known a priori. In this case, an automatic method of building up keywords from short context units such as phones is desirable. We propose a method for the construction of keywords based upon Support Vector Machines. We cast the problem of keyword selection as a feature selection problem for n-grams of phones. We propose an alternating filter-wrappermethod that builds successively longer keywords. Application of this method to language recognition and topic recognition tasks shows that the technique produces interesting and significant qualitative and quantitative results.

Previous Work Gaussian processes for time-series forecasting have been considered before.