Utilizing multiple queues in Greedy Best-First Search (GBFS) has been proven to be a very effective approach to satisficing planning. Successful techniques include extra queues based on Helpful Actions (or Preferred Operators), as well as using Multiple Heuristics. One weakness of all standard GBFS algorithms is their lack of exploration. All queues used in these methods work as priority queues sorted by heuristic values. Therefore, misleading heuristics, especially early in the search process, can cause the search to become ineffective. Type systems, as introduced for heuristic search by Lelis et al, are a development of ideas for exploration related to the classic stratified sampling approach. The current work introduces a search algorithm that utilizes type systems in a new way – for exploration within a GBFS multiqueue framework in satisficing planning. A careful case study shows the benefits of such exploration for overcoming deficiencies of the heuristic. The proposed new baseline algorithm Type-GBFS solves almost 200 more problems than baseline GBFS over all International Planning Competition problems. Type-LAMA, a new planner which integrates Type-GBFS into LAMA-2011, solves 36.8 more problems than LAMA-2011.

The fundamental reasoning problem in RCC-8 is deciding In contrast to the synthetic RCC-8 networks that have the consistency of a set of constraints Θ, i.e., whether there been used in the literature for evaluating the aforementioned is a spatial configuration where the relations between the reasoners, the real-world networks of Table 1 are very sparse regions can be described by Θ. Traditionally in qualitative and one to two orders of magnitude larger. The labels on spatial reasoning (QSR) consistency of such sets is decided their edges contain 1 or 2 base RCC-8 relations forming a by a backtracking algorithm which optionally uses a pathconsistency disjunction. This kind of networks have not been employed algorithm as a preprocessing step for forward in any experimental evaluation of RCC-8 reasoners with the checking. In general, this problem is NPcomplete (Renz exception of (Sioutis and Koubarakis 2012) in which the network and Nebel 1999). However it has been shown in (Renz 1999) adm1 has been used. Typically, the literature focuses that there are tractable subsets of RCC-8 for which the consistency on quite smaller networks (20 to 1000 nodes) with an average problem can be decided by path-consistency. of 4 base RCC-8 relations per edge, and an average Table 1 depicts the characteristics of some real-world node degree ranging from 4 to 20. Deciding the consistency RCC-8 networks recording the topological relations between of real-world networks is a very important task. Inconsistencies administrative regions in Europe (networks nuts, might arise because their RCC-8 relations are computed adm1, and adm2) and the world (networks gadm1 and based on the geometries of geographical objects which gadm2), and the performance of the following reasoners often have not been captured correctly (e.g., overlapping geometries regarding consistency checking: Renz-Nebel01 (Renz and between two regions that in principle are externally Nebel 2001), GQR-1500 (Gantner, Westphal, and Woelfl connected). This is the case for the networks gadm1 and 2008; Westphal and Hué 2012), PPyRCC8 (Sioutis and gadm2.

We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks. The first algorithm filters the tasks according to the rules of the time tabling. The second algorithm performs an overload check that could also be used for the Cumulative constraint. The third algorithm enforces the rules of detectable precedences. The two last algorithms use a new data structure that we introduce and that we call the time line. This data structure provides many constant time operations that were previously implemented in logarithmic time by the Theta-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases.

A wide range of robotic missions contain actions that exhibit looping behavior. Examples of these actions include picking fruit in agriculture, pick-and-place tasks in manufacturing and search patterns in robotic search or survey missions. These looping actions often have a range of acceptable values for the number of loops and a preference function over them. For example, during robotic survey missions, the information gain is expected to increase with the number of loops in a search pattern. Since these looping actions also take time, which is typically bounded, there is a challenge of maximizing utility while respecting time constraints. In this paper, we introduce the Looping Temporal Problem with Preference (LTPP) as a simple parameterized extension of a simple temporal problem. In addition, we introduce a scheduling algorithm for LTPPs which leverages the structure of the problem to find the optimal solution efficiently. We show more than an order of magnitude improvement in run-time over current scheduling techniques and framing a LTPP as a MINLP.

Monte Carlo tree search (MCTS) algorithms are a popular approach to online decision-making in Markov decision processes (MDPs). These algorithms can, however, perform poorly in MDPs with high stochastic branching factors. In this paper, we study state aggregation as a way of reducing stochastic branching in tree search. Prior work has studied formal properties of MDP state aggregation in the context of dynamic programming and reinforcement learning, but little attention has been paid to state aggregation in MCTS. Our main result is a performance loss bound for a class of value function-based state aggregation criteria in expectimax search trees. We also consider how to construct MCTS algorithms that operate in the abstract state space but require a simulator of the ground dynamics only. We find that trajectory sampling algorithms like UCT can be adapted easily, but that sparse sampling algorithms present difficulties. As a proof of concept, we experimentally confirm that state aggregation can improve the finite-sample performance of UCT.

The NP-hard number-partitioning problem is to separate a multiset S of n positive integers into k subsets, such that the largest sum of the integers assigned to any subset is minimized. The classic application is scheduling a set of n jobs with different run times onto k identical machines such that the makespan, the time to complete the schedule, is minimized. We present a new algorithm, cached iterative weakening (CIW), for solving this problem optimally. It incorporates three ideas distinct from the previous state of the art: it explores the search space using iterative weakening instead of branch and bound; generates feasible subsets once and caches them instead of at each node of the search tree; and explores subsets in cardinality order instead of an arbitrary order. The previous state of the art is represented by three different algorithms depending on the values of n and k. We provide one algorithm which outperforms all previous algorithms for k >= 4. Our run times are up to two orders of magnitude faster.

Multi-label learning concerns learning multiple, overlapping, and correlated classes. In this paper, we adapt a recent structured prediction framework called HC-Search for multi-label prediction problems. One of the main advantages of this framework is that its training is sensitive to the loss function, unlike the other multi-label approaches that either assume a specific loss function or require a manual adaptation to each loss function. We empirically evaluate our instantiation of the HC-Search framework along with many existing multi-label learning algorithms on a variety of benchmarks by employing diverse task loss functions. Our results demonstrate that the performance of existing algorithms tends to be very similar in most cases, and that the HC-Search approach is comparable and often better than all the other algorithms across different loss functions.

Markov decision processes (MDP) offer a rich model that has been extensively used by the AI community for planning and learning under uncertainty. However, solving MDPs is often intractable, which has led to the development of many approximate algorithms. In my dissertation work I introduce a new paradigm to handle this complexity by defining a family of MDP reduced models characterized by two parameters: the maximum number of primary outcomes per action that are fully accounted for and the maximum number of occurrences of the remaining exceptional outcomes that are planned for in advance. Reduced models can be solved much faster using heuristic search algorithms, benefiting from the dramatic reduction in the number of reachable states. This framework places recent work on MDP determinization in a broader context and lays the foundation for efficient and systematic exploration of the space of MDP model reductions. Progress so far work includes a formal definition of this family of MDP reductions, a continual planning paradigm to handle the case when the number of exceptions reaches the maximum allowed, a simple greedy approach to generate good reductions for a given planning domain, and a compilation scheme that generates MDP reductions from a PPDDL description of a planning problem.

Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called {\it Dist}. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that {\it Dist} significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, {\it Dist} dramatically outperforms previous local search algorithms and is comparable with complete algorithms.

Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on extensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.