Constraint programming is a natural paradigm for many combinatorial optimisation problems. The complexity of constraint satisfaction for various forms of constraints has been widely-studied, both to inform the choice of appropriate algorithms, and to understand better the boundary between polynomial-time complexity and NP-hardness. In constraint programming it is well-known that any constraint satisfaction problem can be converted to an equivalent binary problem using the so-called dual encoding. Using this standard approach any fixed collection of constraints, of arbitrary arity, can be converted to an equivalent set of constraints of arity at most two. Here we show that this transformation, although it changes the domain of the constraints, preserves all the relevant algebraic properties that determine the complexity. Moreover, we show that the dual encoding preserves many of the key algorithmic properties of the original instance. We also show that this remains true for more general valued constraint languages, where constraints may assign different cost values to different assignments. Hence, we obtain a simple proof of the fact that to classify the computational complexity of all valued constraint languages it suffices to classify only binary valued constraint languages.

When scheduling tasks for field-deployable systems, our solutions must be robust to the uncertainty inherent in the real world. Although human intuition is trusted to balance reward and risk, humans perform poorly in risk assessment at the scale and complexity of real world problems. In this paper, we present a decision aid system that helps human operators diagnose the source of risk and manage uncertainty in temporal problems. The core of the system is a conflict-directed relaxation algorithm, called Conflict-Directed Chance-constraint Relaxation (CDCR), which specializes in resolving over-constrained temporal problems with probabilistic durations and a chance constraint bounding the risk of failure. Given a temporal problem with uncertain duration, CDCR proposes execution strategies that operate at acceptable risk levels and pinpoints the source of risk. If no such strategy can be found that meets the chance constraint, it can help humans to repair the over-constrained problem by trading off between desirability of solution and acceptable risk levels. The decision aid has been incorporated in a mission advisory system for assisting oceanographers to schedule activities in deep-sea expeditions, and demonstrated its effectiveness in scenarios with realistic uncertainty.

As the premier international forumon human-computer interaction, "ACM Conference on Human Factors in ComputingSystems" (CHI), has continued to grow and broaden its range of topics and contributing disciplines. CHI 2014 received over 2000 submissions. Those papers and notes were from diversified research domains — psychologists and computer scientists began to meet new visions from sociology, engineering and manufacturing, communication sciences, design and arts, among others. Here, I would like to introduce progress in HCI research which will bring new opportunities and challenges to AI community.

We present a planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs a new constraint model from domain transition graphs (DTG) of a given planning problem. TCPP encodes the constraint model by using table constraints that allow don't cares or wild cards as cell values. TCPP uses Minion the constraint solver to solve the constraint model and returns the parallel plan. Empirical results exhibit the efficiency of our planning system over state-of-the-art constraint-based planners.

However, it is well-known that applying machine learning and data mining to novel data sets is Finally, inspired by the field of constraint programming, challenging because each application imposes its own requirements (Guns et al. 2013) aim at developing declarative modeling and constraints that often require the development languages for specifying a wide range of mining problems. of new algorithms and systems. While there are software Such languages should support packages and tools such as Scikit for machine learning the high-level and natural modeling of pattern mining and Weka, Orange or Knime for data mining, adapting them tasks; that is, the models should closely correspond to to novel tasks is not easy, which explains why one often resorts the definitions of data mining problems found in the to implementing new algorithms and variations from literature; should support user-defined constraints and scratch.

In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function. The use of DCOPs has gained momentum, due to their capability of addressing complex and naturally distributed problems. A majority of the work in DCOP addresses the resolution problem by detaching the model from the resolution process, where they assume that each agent controls exclusively one variable of the problem (Burke et al. 2006). This assumption often is not reflected in the model specifications, and may lead to inefficient communication requirements. Another limitation of current DCOP resolution methods is their inability to capitalize on the presence of structural information, which may allow incoherent/unnecessary data to reticulate among the agents (Yokoo 2001). The purpose of the proposed dissertation is to study how to adapt and integrate insights gained from centralized solving techniques in order to enhance DCOP performance and scalability, enabling their use for the resolution of real-world complex problems. To do so, we hypothesize that one can exploit the DCOP structure in both problem modeling and problem resolution phases.

The main computational bottleneck in various sampling based and local-search based inference algorithms for Markov logic networks (e.g., Gibbs sampling, MC-SAT, MaxWalksat, etc.) is computing the number of groundings of a first-order formula that are true given a truth assignment to all of its ground atoms. We reduce this problem to the problem of counting the number of solutions of a constraint satisfaction problem (CSP) and show that during their execution, both sampling based and local-search based algorithms repeatedly solve dynamic versions of this counting problem. Deriving from the vast amount of literature on CSPs and graphical models, we propose an exact junction-tree based algorithm for computing the number of solutions of the dynamic CSP, analyze its properties, and show how it can be used to improve the computational complexity of Gibbs sampling and MaxWalksat. Empirical tests on a variety of benchmarks clearly show that our new approach is several orders of magnitude more scalable than existing approaches.

Boolean SATisfiability (SAT) is an important problem in AI. SAT solvers have been effectively used in important industrial applications including automated planning and verification. In this paper, we present novel algorithms for fast SAT solving by employing two common subclause elimination (CSE) approaches. Our motivation is that modern SAT solving techniques can be more efficient on CSE-processed instances. Empirical study shows that CSE can significantly speed up SAT solving.

The problem of planning or discrete control for timed system has earlier been solved with various constraint-based solution methods, including Constraint Programming, SAT solvers, SAT modulo Theories solvers, and Mixed Integer-Linear Programming. In this work we investigate the encoding of time in such constraint-based representations. A main issue with existing encodings is the necessity to allow arbitrary interleavings of concurrent actions' starting and ending times. The complex combinatorics of this can lead to poor scalability of leading search methods. We show how real or rational time in temporal models can in many practically important cases be replaced by integer time, and how this leads to far simpler encodings of planning as constraints. We demonstrate that the simplified encodings substantially improve the scalability of constraint-based planning.

Tractable classes constitute an important issue in Artificial Intelligence to define new islands of tractability for reasoning or problem solving. In the area of constraint networks, numerous tractable classes have been defined, and recently, the Broken Triangle Property (BTP) has been shown as one of the most important of them, this class including several classes previously defined. In this paper, we propose a new class called ETP for Extendable-Triple Property, which generalizes BTP, by including it. Combined with the verification of the Strong-Path-Consistency, ETP is shown to be a new tractable class. Moreover, this class inherits some desirable properties of BTP including the fact that the instances of this class can be solved thanks to usual algorithms (such as MAC or RFL) used in most solvers. We give the theoretical material about this new class and we present an experimental study which shows that from a practical viewpoint, it seems more usable in practice than BTP.