We show that GaP can be rewritten free of the score/activation matrix. This gives us new insights about the estimation of the topic/dictionary matrix by maximum marginal likelihood estimation. In particular, this explains the robustness of this estimator to over-specified values of the factorization rank and in particular its ability to automatically prune spurious dictionary columns, as empirically observed in previous work. The marginalization of the activation matrix leads in turn to a new Monte-Carlo Expectation-Maximization algorithm with favorable properties.

In this paper, the problem of maximizing a black-box function $f:\mathcal{X} \to \mathbb{R}$ is studied in the Bayesian framework with a Gaussian Process (GP) prior. In particular, a new algorithm for this problem is proposed, and high probability bounds on its simple and cumulative regret are established. The query point selection rule in most existing methods involves an exhaustive search over an increasingly fine sequence of uniform discretizations of $\mathcal{X}$. The proposed algorithm, in contrast, adaptively refines $\mathcal{X}$ which leads to a lower computational complexity, particularly when $\mathcal{X}$ is a subset of a high dimensional Euclidean space. In addition to the computational gains, sufficient conditions are identified under which the regret bounds of the new algorithm improve upon the known results. Finally an extension of the algorithm to the case of contextual bandits is proposed, and high probability bounds on the contextual regret are presented.

This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific. It would be quite a daunting task to manually monitor prices and make bidding decisions at all web sites currently offering the camera--especially if accessories such as a flash and a tripod are sometimes bundled with the camera and sometimes auctioned separately. However, for the next generation of trading agents, autonomous bidding in simultaneous auctions will be a routine task.

This article examines the direction in which knowledge bases are constructed for diagnosis and decision making When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses However, experts usually find it simpler to reason in the opposite direction-from hypotheses to unobservable evidence-because this direction reflects causal relationships Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty We illustrate this concept with influence diagrams, a methodology for graphically representing a joint probability distribution Influence diagrams provide a practical means by which an expert can characterize the qualitative and quantitative relationships among evidence and hypotheses in the appropriate direction Once constructed, the relationships can easily be reversed into the less intuitive direction in order to perform inference and diagnosis, In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation "OK," we replied, "If the tiger were present, what is the probability that you would see that image? On the other hand, if the tiger were not present, what is the probability you would see it?" Before we could say "what is the probability there is a tiger in the first place?" Since then, we have pondered this question. Why is it that we want to look at problems of evidential reasoning backward?

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

Today, mobile robotics is an increasingly important bridge between the two areas. It is advancing the theory and practice of cooperative cognition, perception, and action and serving to reunite planning techniques with sensing and real-world performance. Further, developments in mobile robotics can have important practical economic and military consequences. For some time now, amateurs, hobbyists, students, and researchers have had access to how-to books on the low-level mechanical and electronic aspects of mobile-robot construction (Everett 1995; McComb 1987). The famous Massachusetts Institute of Technology (MIT) 6.270 robot-building course has contributed course notes and hardware kits that are now available commercially and in the form of an influential book (Jones 1998; Jones and Flynn 1993).

The American Association for Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections. Research in cognitive robotics is concerned with the theory and implementation of robots that reason, act, and perceive in changing incompletely known, unpredictable environments. Such robots must have higherlevel cognitive functions that involve, for example, reasoning about goals, actions, the cognitive states of other agents, and time as well as when to perceive and what to look for.

It was started in 2005 and is an annual event that attracts between 5 and 10 teams. It has since been organized by the AI group at Clausthal University of Technology. MAPC is not collocated with any other event. Using our MASSim platform, the participants are running their own systems locally and only interact with the tournament server over the Internet. A steering committee oversees the whole process and determines the organization committee. The scenario changes every other year: the current one is "Agents on Mars." The goal was to implement a team of heterogeneous, cooperating agents to occupy zones on planet Mars. The infrastructure on Mars is given by a directed graph (300 nodes). Agents could take on roles (explorer, sentinel, saboteur, repairer, inspector) and needed to cooperate in an environment with incomplete knowledge so as to win against a competing team: the graph was not known, and each action comes at a price. Conquered terrain brings in money to improve agents. The timeline of the contest is as follows.

Homeless youth are prone to human immunodeficiency virus (HIV) due to their engagement in high-risk behavior such as unprotected sex, sex under influence of drugs, and so on. Many nonprofit agencies conduct interventions to educate and train a select group of homeless youth about HIV prevention and treatment practices and rely on word-of-mouth spread of information through their one single social network Previous work in strategic selection of intervention participants does not handle uncertainties in the social networks' structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision-support system to aid the agencies in this task. PSINET includes the following key novelties: (1) it handles uncertainties in network structure and evolving network state; (2) it addresses these uncertainties by using POMDPs in influence maximization; and (3) it provides algorithmic advances to allow high-quality approximate solutions for such POMDPs. Simulations show that PSINET achieves around 60 percent more information spread over the current state of the art.

A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly surveys approximation methods, and closes with a brief discussion of open issues. Often, truth is more elusive, and categorical statements can only be made by judgment of the likelihood or other ordinal attribute of competing propositions. Probability theory is the oldest and best-understood theory for representing and reasoning about such situations, but early AI experimental efforts at applying probability theory were disappointing and only confirmed a belief among AI researchers that those who worried about numbers were "missing the point."