The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem.

The Optiplan planning system is the first integer programming-based planner that successfully participated in the international planning competition. This engineering note describes the architecture of Optiplan and provides the integer programming formulation that enabled it to perform reasonably well in the competition. We also touch upon some recent developments that make integer programming encodings significantly more competitive.

We describe the version of the GPT planner used in the probabilistic track of the 4th International Planning Competition (ipc-4). This version, called mGPT, solves Markov Decision Processes specified in the ppddl language by extracting and using different classes of lower bounds along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations where the alternative probabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state and the greedy policy.

Year 2006 is the 150th anniversary of Ivan Franko (1856-1916), the prominent Ukrainian writer, poet, publicist, philosopher, sociologist, economist, translator-polyglot and the public figure. His incomplete collected works were published in 50 volumes (Franko, 1976-86). With this name the notion of national identity in the Western Ukraine is connected. Franko's works have intensive plot and interesting topic.

When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed by maintaining this proper distinction are often prohibitive, one asks for conditions under which it can be safely ignored. Such conditions are given by the missing at random (mar) and coarsened at random (car) assumptions. In this paper we provide an in-depth analysis of several questions relating to mar/car assumptions. Main purpose of our study is to provide criteria by which one may evaluate whether a car assumption is reasonable for a particular data collecting or observational process. This question is complicated by the fact that several distinct versions of mar/car assumptions exist. We therefore first provide an overview over these different versions, in which we highlight the distinction between distributional and coarsening variable induced versions. We show that distributional versions are less restrictive and sufficient for most applications. We then address from two different perspectives the question of when the mar/car assumption is warranted. First we provide a ''static'' analysis that characterizes the admissibility of the car assumption in terms of the support structure of the joint probability distribution of complete data and incomplete observations. Here we obtain an equivalence characterization that improves and extends a recent result by Grunwald and Halpern. We then turn to a ''procedural'' analysis that characterizes the admissibility of the car assumption in terms of procedural models for the actual data (or observation) generating process. The main result of this analysis is that the stronger coarsened completely at random (ccar) condition is arguably the most reasonable assumption, as it alone corresponds to data coarsening procedures that satisfy a natural robustness property.

The 2004 International Planning Competition, IPC-4, included a probabilistic planning track for the first time. We describe the new domain specification language we created for the track, our evaluation methodology, the competition domains we developed, and the results of the participating teams.

Most autonomous robots are equipped with restricted, unreliable, and inaccurate sensors and effectors and operate in complex and dynamic environments. A successful approach to deal with the resulting uncertainty is the use of controllers that prescribe the robots' behavior in terms of concurrent reactive plans (CRPs) -- plans that specify how the robots are to react to sensory input in order to accomplish their jobs reliably (e.g., McDermott, 1992a; Beetz, 1999). Reactive plans are successfully used to produce situation specific behavior, to detect problems and recover from them automatically, and to recognize and exploit opportunities (Beetz et al., 2001). These kinds of behaviors are particularly important for autonomous robots that have only uncertain information about the world, act in dynamically changing environments, and are to accomplish complex tasks efficiently. Besides reliability and flexibility, foresight is another important capability of competent autonomous robots (McDermott, 1992a).

Alonzo Church and Alan Turing The items in this collage were selected September 26, 2005 (www.latimes.com). But there is a realm beyond the exhibit chiefly covers the 50 years of Grand Challenge played out, with 195 the classical computer: the quantum. The efforts to teach a machine to play a teams entering the competition, five probabilistic nature of quantum theory allows quintessentially human pastime culminating teams successfully completing the course atoms and other quantum objects to in the Deep Blue-Kasparov match." The New York and even highschool students but can also be 0 and 1 at the same Times. What are the Limits of Learning .com). "The Stanford scientists who led the vehicles.

The AAAI-06 workshops will be held rather than a regular AAAI paper.

(ESWC 2006). (IIS 2006).