List of organizers of the Twenty-First International Conference on Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011.

This volume contains the papers accepted for presentation at ICAPS 2011, the Twenty-First International Conferenceon Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011. The annual ICAPS conference series was established in 2003 through the merger of two pre-existing biennial conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). ICAPS continues the traditional high standards of AIPS and ECP as an archival forum for new research in the rapidly developing field of automated planning andscheduling. This volume contains the papers accepted at the conference.

Bayesian Network Classifiers are popular approaches for classification problems where instances have to be assigned to one of several classes. However, in many domains, it is necessary to assign instances to multiple classes at the same time. This task has been normally addressed either by (i) transforming the problem into a single-class scenario by defining a new class variable with all of the possible combinations of classes or, (ii) by building an independent classifier for each class variable. Either way, the resulting models do not capture all the relations and dependencies between classes and features resulting into unprecise multidimensional classifiers. In this paper, we introduce a two-step method for learning Multidimensional Bayesian Network Classifiers (MBC) from data based on mutual information measures. The first step of the method learns an initial MBC structure which then, in the second step, is refined. Our approach is simple and keeps all the interactions and dependencies among classes and features. The method was tested on three benchmark multidimensional data-sets. Preliminary experimental results show how our method outperforms state-of-the-art methods used in multidimensional classification.

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typically the case that most of the knowledge in the system is likely to be correct as specified by the expert(s) and/or knowledge engineer(s). In this paper, we propose a method to identify the possible changes by understanding the contribution of parameters on the outputs of concern. Our approach is based on Bayesian Knowledge Bases for modeling uncertainties. We start with single parameter changes and then extend to multiple parameters. In order to identify the optimal solution that can minimize the change to the model as specified by the domain experts, we define and evaluate the sensitivity values of the results with respect to the parameters. We discuss the computational complexities of determining the solution and show that the problem of multiple parameters changes can be transformed into Linear Programming problems, and thus, efficiently solvable. Our work can also be applied towards validating the knowledge base such that the updated model can satisfy all test-cases collected from the domain experts.

The Partially Observable Markov Decision Process (POMDP) provides a rich mathematical model for designing agents that have to formulate plans under uncertainty. The curses of dimensionality and history associated with solving POMDPs have lead to numerous refinements of the value iteration algorithm. Several exact methods with different pruning strategies have been devised, yet, limited scalability has lead research to focus on ways to approximate the optimal value function. One set of approximations relies on point-based value iteration, which maintains a fixed-size value function, and is typically executed offline. Another set of approximations relies on tree search, which explores the implicit tree defined by the value iteration equation, and is typically executed online. In this paper we present a hybrid value iteration algorithm that combines the benefits of point-based value iteration and tree search. Using our approach, a hybrid agent executes tree search online, and occasionally updates its offline-computed lower bound on the optimal value function, resulting in improved lookahead and higher obtained reward, while meeting real-time constraints. Thus, unlike other hybrid algorithms that use an invariant value function computed offline, our proposed scheme uses information from the real-time tree search process to reason whether to perform a point-based backup online. Keeping track of partial results obtained during online planning makes the computation of point-based backups less prohibitive. We report preliminary results that support our approach.

Defeasible argumentation and default reasoning are usually perceived as two similar, but distinct approaches to commonsense reasoning. In this paper, we combine these two fields by viewing (defeasible resp. default) rules as a common crucial part in both areas. We will make use of possible worlds semantics from default reasoning to provide examples for arguments, and carry over the notion of plausibility to the argumentative framework. Moreover, we base a priority relation between arguments on the tolerance partitioning of system Z and obtain a criterion phrased in system Z terms that ensures warrancy in defeasible argumentation.

Multiply Sectioned Bayesian Networks (MSBNs) extend single-agent Bayesian networks to the setting of multi-agent probabilistic reasoning. The MSBN global propagation is conducted through inter-agent message passing, coupled with intra-agent (local) message passing at local domains. Existing LJF-based MSBN inference algorithms require repeated full-scale local propagation, which may cause bottlenecks in a non-sparse network. We propose a novel method that conducts 1) delayed inter-agent message manipulation, and 2) partial local message propagation. Analysis shows that our approach significantly reduces the amount of local computation while maintaining the correctness of MSBN global propagation.

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning, that result in much simpler and efficient models in some domains. However, methods for learning this type of models from data have not been developed. In this paper we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method has three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data. Our method obtains the best score in terms of the structure and a competitive predictive accuracy.

Causality plays an important role in our comprehension of the world. It amounts to determine what truly causes what and what it matters. Interventions allow the identification of elements in a sequence of events that are related in a causal way. In this paper, we introduce belief causation and we proposea method for handling interventions in graphical model under an uncertain environment where the uncertainty is represented by belief masses, so-called belief causal networks. More specifically, we propose a generalization of the “DO” operator and explain the needed changes on the structure of the graph to model a belief causal network on which interventions are proceeded.

Teaching an upper-level undergraduate robotics course at Historically Black Colleges and Universities (HBCUs) is challenging. The lack of suitable teaching materials is one of the biggest challenges, although there are many great masterpieces in developing robotics course materials, which are, however, generally developed for teaching students at elite Research I universities. This paper presents ideas and details in adopting and revising these course materials and creating upper-level undergraduate robotics course materials that are suitable for underrepresented students.