Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinforcementlearning algorithm for learning in these domains and prove for certain environments the algorithm is probably approximately correct with a sample complexity that scales polynomially with the state-space dimension. Unfortunately, no optimal planning techniques exist in general for such problems; instead we use fitted value iteration to solve the learned MDP, and include the error due to approximate planning in our bounds. Finally, we report an experiment using a robotic car driving over varying terrain to demonstrate that these dynamics representations adequately capture real-world dynamics and that our algorithm can be used to efficiently solve such problems.

Assume that cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model.We consider the identification problem of total effects in the presence of latent variables and selection bias between a treatment variable and a response variable. Pearl and his colleagues provided the back door criterion, the front door criterion (Pearl, 2000) and the conditional instrumental variable method (Brito and Pearl, 2002) as identifiability criteria for total effects in the presence of latent variables, but not in the presence of selection bias. In order to solve this problem, we propose new graphical identifiability criteria for total effects based on the identifiable factor models. The results of this paper are useful to identify total effects in observational studies and provide a new viewpoint to the identification conditions of factor models.

We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) representation. If the decision of an agent participates in no reasoning patterns, then we can effectively ignore that decision for the purpose of calculating a Nash equilibrium for the game. In some cases, this can lead to exponential time savings in the process of equilibrium calculation. Moreover, our algorithm can be used to enumerate the reasoning patterns in a game, which can be useful for constructing more effective computerized agents interacting with humans.

Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure.

The problem of early word-learning has been subject of philosophical controversy for centuries [1]. The always visionary Augustine argued that the child makes the connections between words and their referents by understanding the referential intentions of others, thus anticipating the modern theory of mind in about fifteen centuries [2]. In the 17th century, Locke's empiricism supported the associationist viewpoint, which contends that the mechanism of word learning is sensitivity to covariation, i.e., if two events occur at the same time, they become associated. Here we examine a radical offshoot of the associationist approach to lexicon acquisition termed crosssituational or observational learning [3], which asserts that the meaning of a word can be determined by looking for something in common across all observed uses of that word [4]. In other words, learning takes place through the statistical sampling of the contexts in which a word appears.

We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an $\ell_2$ penalty. We show that this new {\em $k$-support norm} provides a tighter relaxation than the elastic net and is thus a good replacement for the Lasso or the elastic net in sparse prediction problems. Through the study of the $k$-support norm, we also bound the looseness of the elastic net, thus shedding new light on it and providing justification for its use.

From this excellent collection of papers, three for presentation at ICAPS 2012, the were selected for special recognition. ICAPS continues Nguyen, Vien Tran, Tran Cao Son and Enrico the traditional high standards of AIPS and ECP Pontelli were selected for Best Student Paper as an archival forum for new research in the Award. In addition to the oral presentation of these e 45 papers included in this volume, consisting papers, the technical program of this year's of 37 long papers and 8 short papers, are ICAPS conference includes invited talks by those selected for plenary presentation at three distinguished speakers: Robert O. Ambrose ICAPS 2012 from a total of 132 submissions. Topics under various constraints and assumptions, included real-time planning, planning in mixed to empirical evaluation of planning and discrete-continuous domains, planning for systems scheduling techniques in practical applications. Papers in the subareas of optimal planning, probabilistic were encouraged from a range of neighboring and non-deterministic planning, planning disciplines, including model-based and scheduling for transportation, robot path reasoning, hybrid systems, run-time verification, planning, and new developments in heuristics control and robotics.

The control of dynamic systems, which aims to minimize the deviation of state variables from reference values in a continuous state space, is a central domain of cybernetics and control theory. The objective of action planning is to find feasible state trajectories in a discrete state space from an initial state to a state satisfying the goal conditions, which in principle addresses the same issue on a more abstract level. We combine these approaches to switch between dynamic system characteristics on the fly, and to generate control input sequences that affect both discrete and continuous state variables. Our approach (called Domain Predictive Control) is applicable to hybrid systems with linear dynamics and discretizable inputs.

This paper aims at incorporating tactical aspects of oil pipeline networks to the supply chain planning model. The strategic design of supply chains is covered in literature by well understood and recurring patterns such as multi-commodity networks, dynamic parameters over time, capacity on facilities, transportation capacity or facilities with demand, production and inventory. We consider the following characteristics: capacity for in-transit inventory, transit time and flow reversal. Our objective is a better estimate for resources required by the network and therewith allow a more precise optimization of their use. All aspects are modeled to be efficiently solved by linear programming algorithms.

Partial order reduction is a state space pruning approach that has been originally introduced in computer aided verification. Recently, various partial order reduction techniques have also been proposed for planning. Despite very similar underlying ideas, the relevant literature from computer aided verification has hardly been analyzed in the planning area so far, and it is unclear how these techniques are formally related. We provide an analysis of existing partial order reduction techniques and their relationships. We show that recently proposed approaches in planning are instances of general partial order reduction approaches from computer aided verification. Our analysis reveals a hierarchy of dominance relationships and shows that there is still room for improvement for partial order reduction techniques in planning. Overall, we provide a first step towards a better understanding and a unifying theory of partial order reduction techniques from different areas.