This is a summary of the journal article published by Journal on Computing entitled "An Optimal Constraint Programming Approach to the Open-Shop Problem." The article presents an optimal constraint programming approach for the Open-Shop scheduling problem, which integrates recent constraint propagation and branching techniques with new upper bound heuristics. Randomized restart policies combined with nogood recording allow to search diversification and learning from restarts. This approach is compared with the best-known metaheuristics and exact algorithms, and shows better results on a wide range of benchmark instances.

This is a summary of Lombardi and Milano, 2012, where we propose a novel method for Minimal Critical Set identification, to be used for the solution of scheduling problems via Precedence Constraint Posting. The method is based on a minimum-flow problem and a heuristic minimization step. The proposed approach is much more scalable than enumeration-based MCS detection, faster and easier to implement than enveloped-based sampling, more versatile than earliest-start-schedule sampling. As a second major contribution, the paper contains a thorough comparison (on the PSPLIB) of MCS detection method, which outlines their individual strengths and weakness. Additionally, the experimentation provides novel insight in the effectiveness of a widely employed MCS ranking heuristic.

We present a general and detailed development of an algorithm for finite-horizon fitted-Q iteration with an arbitrary number of reward signals and linear value function approximation using an arbitrary number of state features. This includes a detailed treatment of the 3-reward function case using triangulation primitives from computational geometry and a method for identifying globally dominated actions. We also present an example of how our methods can be used to construct a real-world decision aid by considering symptom reduction, weight gain, and quality of life in sequential treatments for schizophrenia. Finally, we discuss future directions in which to take this work that will further enable our methods to make a positive impact on the field of evidence-based clinical decision support.

In this paper we show the identification between stochastic optimal control computation and probabilistic inference on a graphical model for certain class of control problems. We refer to these problems as Kullback-Leibler (KL) control problems. We illustrate how KL control can be used to model a multi-agent cooperative game for which optimal control can be approximated using belief propagation when exact inference is unfeasible.

Flight controllers manage the orientation and modes of eight large solar arrays that power the International Space Station (ISS). The task requires generating plans that balance complex constraints and preferences. These considerations include context-dependent constraints on viable solar array configurations, temporal limits on transitions between configurations, and preferences on which considerations have priority. The Solar Array Constraint Engine (SACE) treats this operations planning problem as a sequence of tractable constrained optimization problems. SACE uses constraint management and automated planning capabilities to reason about the constraints, to find optimal array configurations subject to these constraints and solution preferences, and to automatically generate solar array operations plans.

Business processes under authorization control are sets of coordinated activities subject to a security policy stating which agent can access which resource. Their behavior is difficult to predict due to the complex and unexpected interleaving of different execution flows within the process. Therefore, serious flaws may go undetected and manifest themselves only after deployment. This problem may be tackled by applying formal methods to reason about business process models. In this paper we outline the main contributions in this application domain of (Armando et al. 2012), that uses the action-based planning language C and the Causal Calculator tool CCalc. C is used to specify a business process from the banking domain that is representative of an important class of business processes of practical relevance, and proved to be a rich and natural formal specification language in this domain. CCalc is then used to automatically solve three reasoning tasks that arise in this context. We also compare C with the SMV specification language used in model-checking: the comparison highlights some key advantages of C in the business process domain.

Many renewable sources of energy can harness greater uptime and power output when located in remote and potentially hostile locations. One example of this is wind power, wherein turbines positioned at offshore locations can experience higher and more sustained windspeeds than their onshore counterparts. However, these traits also lead to increased load and degradation upon components, which in turn means that regular maintenance is required. While onshore maintenance costs are relatively trivial, the costs associated with offshore maintenance can be several orders-of-magnitude greater. Traditionally, the scheduling of these repairs is performed by hand using a set of pre-determined plans for specific fault-categories (e.g. trivial/minor/major component replacement). This paper formulates this problem as a PDDL domain which encapsulates all of the individual pre-defined plans in a single representation, such that multiple levels of response can be integrated in a single plan. The domain presented is complex in that it contains not only numeric and temporal planning aspects, but that a subset of the domain is heavily geared towards pure scheduling. We include performance results on how a state-of-the-art planner performs on various example scenarios.

This paper proposes a new model and algorithm for task sequencing in remote laser welding in the automotive industry. It is shown that task sequencing (in which order to weld the seams) is strongly related to path planning (how the welding robot should move), therefore the two problems must be solved together, in an integrated way. The problem is modeled as a direct product of a traveling salesman and a path planning problem, and a tabu search algorithm is proposed for solving it. Computational experiments show that the proposed method leads to a substantial reduction in the cycle time of the welding operation compared to an earlier approach.

Unit Commitment is a fundamental problem in power systems engineering, deciding which generating units to switch on, and when to switch them on, in order to efficiently meet anticipated demand. It has traditionally been solved as a Mixed Integer Programming (MIP) problem but upcoming changes to the power system drastically increase the MIP solution time. In this paper, we discuss the benefits that using planning may have over the established methods.We provide a formal description of Unit Commitment, and we present its formulation as MIP and as a planning problem. This is a novel and interesting application area for planning, with features that make the domain challenging for current planners.

Goal-driven automated composition of software components is an important problem with applications in Web service composition and stream processing systems. The popular approach to address this problem is to build the composition automatically using AI planning. However, it is shown that some of these planning approaches may neither be feasible nor scalable for many large-scale flow-based applications. Recent advances have proven that the automated composition problem can take advantage of expert knowledge describing the many ways in which different reusable components can be composed. This knowledge can be represented using an extensible composition template or pattern. In prior work, a flow pattern language called Cascade and its corresponding specialized planner have shown the best performance in these domains. In this paper, we propose the use of Hierarchical Task Network (HTN) planning for the composition of stream processing applications. To this end, we propose an automated approach of creating an HTN-based problem from the Cascade representation of the flow patterns. The resulting technique not only allows us to use the HTN planning paradigm and its many advantages including added expressivity but also enables optimization and customization of composition with respect to preferences and constraints. Further, we propose and develop a lookahead heuristic and show that it significantly reduces the planning time. We have performed extensive experimentation with stream processing applications and evaluated applicability and performance of our approach.