Consider a family whose home is equipped with several service robots. The actions planned for the robots must adhere to Interaction Constraints (ICs) relating them to human activities and preferences. These constraints must be sufficiently expressive to model both temporal and logical dependencies among robot actions and human behavior, and must accommodate incomplete information regarding human activities. In this paper we introduce an approach for automatically generating plans that are conformant wrt. given ICs and partially specified human activities. The approach allows to separate causal reasoning about actions from reasoning about ICs, and we illustrate the computational advantage this brings with experiments on a large-scale (semi-)realistic household domain with hundreds of human activities and several robots.

A Simple Temporal Network with Uncertainty (STNU) is a structure for representing and reasoning about temporal constraints in domains where some temporal durations are not controlled by the executor. The most important property of an STNU is whether it is dynamically controllable (DC) whether there exists a strategy for executing the controllable time-points that guarantees that all constraints will be satisfied no matter how the uncontrollable durations turn out. This paper provides a novel mapping from STNUs to Timed Game Automata (TGAs) that: (1) explicates the deep theoretical relationships between STNUs and TGAs; and (2) enables the memoryless strategies generated from the TGA to be transformed into equivalent STNU execution strategies that reduce the real-time computational burden for the executor. The paper formally proves that the STNU-to-TGA encoding properly captures the execution semantics of STNUs.

The paper proposes a dynamic method, Recursive SBDS(ReSBDS), for efficient partial symmetry breaking. Wefirst demonstrate how (partial) Symmetry BreakingDuring Search (SBDS) misses important pruning opportunitieswhen given only a subset of symmetries tobreak. The investigation pinpoints the culprit and in turnsuggests rectification. The main idea is to add extra conditionalconstraints during search recursively to prunealso symmetric nodes of some pruned subtrees. Thus,ReSBDS can break extra symmetry compositions, butis carefully designed to break only the ones that areeasy to identify and inexpensive to break. We presenttheorems to guarantee the soundness and terminationof our approach, and compare our method with popularstatic and dynamic methods. When the variable (value)heuristic is static, ReSBDS is also complete in eliminatingall interchangeable variables (values) given only thegenerator symmetries. Extensive experimentations confirmthe efficiency of ReSBDS, when compared againststate of the art methods.

A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios.

The concepts of MSS (Maximal Satisfiable Subset) andCoMSS (also called Minimal Correction Subset) playa key role in many A.I. approaches and techniques. Inthis paper, a novel algorithm for partitioning a BooleanCNF formula into one MSS and the correspondingCoMSS is introduced. Extensive empirical evaluationshows that it is more robust and more efficient on mostinstances than currently available techniques.

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.

Given a set of axis-parallel n-dimensional boxes, the q-intersection is defined as the smallest box encompassing all the points that belong to at least q boxes. Computing the q-intersection is a combinatorial problem that allows us to handle robust parameter estimation with a numerical constraint programming approach. The q-intersection can be viewed as a filtering operator for soft constraints that model measurements subject to outliers. This paper highlights the equivalence of this operator with the search of q-cliques in a graph whose boxicity is bounded by the number of variables in the constraint network. We present a computational study of the q-intersection. We also propose a fast heuristic and a sophisticated exact q-intersection algorithm. First experiments show that our exact algorithm outperforms the existing one while our heuristic performs an efficient filtering on hard problems.

We present a novel approach to low-dimensional neighbor embedding for visualization, based on formulating an information retrieval based neighborhood preservation cost function as Maximum satisfiability on a discretized output display. The method has a rigorous interpretation as optimal visualization based on the cost function. Unlike previous low-dimensional neighbor embedding methods, our formulation is guaranteed to yield globally optimal visualizations, and does so reasonably fast. Unlike previous manifold learning methods yielding global optima of their cost functions, our cost function and method are designed for low-dimensional visualization where evaluation and minimization of visualization errors are crucial. Our method performs well in experiments, yielding clean embeddings of datasets where a state-of-the-art comparison method yields poor arrangements. In a real-world case study for semi-supervised WLAN signal mapping in buildings we outperform state-of-the-art methods.

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.