Moreover, coming up with possible failure scenarios that may arise during system operation and determining triggers for these cases is a major challenge. ABB Robotics' painting robots are advanced distributed systems that must be thoroughly tested before they are shipped to customers, mostly from the automotive and avionics domains. Improving the overall quality of painting robots has always been of utmost importance because it is viewed as a competitive advantage in the market for industrial robots -- an error in the control system of these robots, uncovered in the field, can have serious economic consequences. After some tuning, the model was able to occur when the robot's speed increases and switching generate test scenarios that could find all the re-introduced between different paint spray patterns occurs too bugs. This was considered a significant success rapidly.

A qualitative representation $\phi$ is like an ordinary representation of a relation algebra, but instead of requiring $(a; b)^\phi = a^\phi | b^\phi$, as we do for ordinary representations, we only require that $c^\phi\supseteq a^\phi | b^\phi \iff c\geq a ; b$, for each $c$ in the algebra. A constraint network is qualitatively satisfiable if its nodes can be mapped to elements of a qualitative representation, preserving the constraints. If a constraint network is satisfiable then it is clearly qualitatively satisfiable, but the converse can fail. However, for a wide range of relation algebras including the point algebra, the Allen Interval Algebra, RCC8 and many others, a network is satisfiable if and only if it is qualitatively satisfiable. Unlike ordinary composition, the weak composition arising from qualitative representations need not be associative, so we can generalise by considering network satisfaction problems over non-associative algebras. We prove that computationally, qualitative representations have many advantages over ordinary representations: whereas many finite relation algebras have only infinite representations, every finite qualitatively representable algebra has a finite qualitative representation; the representability problem for (the atom structures of) finite non-associative algebras is NP-complete; the network satisfaction problem over a finite qualitatively representable algebra is always in NP; the validity of equations over qualitative representations is co-NP-complete. On the other hand we prove that there is no finite axiomatisation of the class of qualitatively representable algebras.

A challenging Earth-observing satellite scheduling problem was recently studied in (Frank, Do and Tran 2016) for which the best resolution approach so far on the proposed benchmark is a time-indexed Mixed Integer Linear Program (MILP) formulation. This MILP formulation produces feasible solutions but is not able to prove optimality or to provide tight optimality gaps, making it difficult to assess the quality of existing solutions. In this paper, we first introduce an alternative disjunctive MILP formulation that manages to close more than half of the instances of the benchmark. This MILP formulation is then relaxed to provide good bounds on optimal values for the unsolved instances. We then propose a CP Optimizer model that consistently outperforms the original time-indexed MILP formulation, reducing the optimality gap by more than 4 times. This Constraint Programming (CP) formulation is very concise: we give its complete OPL implementation in the paper. Some improvements of this CP model are reported resulting in an approach that produces optimal or near-optimal solutions (optimality gap smaller than 1%) for about 80% of the instances. Unlike the MILP formulations, it is able to quickly produce good quality schedules and it is expected to be flexible enough to handle the changing requirements of the application.

We investigate solvable-unsolvable phase transitions in the single-machine scheduling (SMS) problem. SMS is at the core of practical problems such as telescope and satellite scheduling and manufacturing. To study the solvability phase transition, we construct a variety of instance families param-eterized by the set of the processing times, the window size (deadline minus release time), and the horizon. We empirically establish the phase transition and look for an easy-hard- easy pattern for this family using several common solvers. While in many combinatorial problems a phase transition coincides with typically hard instances, whether or not that is the case with SMS remains an open question, and merits further study.

Energy costs are an increasingly important issue in real-world scheduling, for both economic and environmental reasons. This paper deals with a variant of the well-known job shop scheduling problem, where we consider a bi-objective optimization of both the weighted tardiness and the energy costs. To this end, we design a hybrid metaheuristic that combines a genetic algorithm with a novel local search method and a linear programming approach. We also propose an efficient procedure for improving the energy cost of a given schedule. In the experimental study we analyse our proposal and compare it with the state of the art and also with a constraint programming approach, obtaining competitive results.

Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the ''envelope'' of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is not complete. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints, while assigning the discrete variables statically it is not.

The problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the $\textit{inflation technique}$ for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the constraints at the level of inflation are weak, such as observable statistical independences implied by disjoint causal ancestry, the translated constraints can be strong. We apply this method to derive new inequalities whose violation by a distribution witnesses that distribution's incompatibility with the causal structure (of which Bell inequalities and Pearl's instrumental inequality are prominent examples). We describe an algorithm for deriving all such inequalities for the original causal structure that follow from ancestral independences in the inflation. For three observed binary variables with pairwise common causes, it yields inequalities that are stronger in at least some aspects than those obtainable by existing methods. We also describe an algorithm that derives a weaker set of inequalities but is more efficient. Finally, we discuss which inflations are such that the inequalities one obtains from them remain valid even for quantum (and post-quantum) generalizations of the notion of a causal model.

We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identification of arbitrary causal effects when the new models contain directed and undirected edges but no bidirected edge. We also provide a necessary and sufficient graphical criterion for the identification of the causal effect of a single variable on the rest of the variables. Moreover, we develop an exact algorithm for learning the new models from observational and interventional data via answer set programming. Finally, we introduce gated models for causal effect identification, a new family of graphical models that exploits context specific independences to identify additional causal effects. Keywords: Acyclic directed mixed graphs; causal models; answer set programming.

This paper presents a general framework for analyzing heuristics for constraint solving, including backtracking and arc consistency algorithms. It will emphasize heuristics for variable selection during search, since this is where major differences are found. In earlier work two basic approaches to this problem were developed.The first was a general theoretical framework for different types of heuristics, which characterized ideal performance so that the actual performance of heuristics could be compared to this standard. The second involved the discovery that, while there are a large number of features that can be used for heuristic decisions in variable ordering, differences in effectiveness boil down to only two basic "heuristic actions". The present paper applies basic ideas from decision analysis to characterize these two approaches to better understand their status and interrelations. It shows that the first is essentially a normative decision analysis, and that models of this sort imply general prescriptive principles (notably the Fail-First Principle). The second is concerned with descriptive models of actual performance.

Privacy has been a major motivation for distributed problem optimization. However, even though several methods have been proposed to evaluate it, none of them is widely used. The Distributed Constraint Optimization Problem (DCOP) is a fundamental model used to approach various families of distributed problems. Here we approach the problem by letting both the optimized costs found in DCOPs and the privacy requirements guide the agents' exploration of the search space. We introduce Utilitarian Distributed Constraint Optimization Problem (UDCOP) where the costs and the privacy requirements are used as parameters to a heuristic modifying the search process. Common stochastic algorithms for decentralized constraint optimization problems are evaluated here according to how well they preserve privacy.