We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical.

When a high-level constraint programming (CP) language lacks a (possibly global) constraint that would allow the formulation of a particular model of a combinatorial problem, then the modeller traditionally has the choice of (1) switching to another CP language that has all the required constraints, (2) formulating a different model that does not require the lacking constraints, or (3) implementing the lacking constraint in the low-level implementation language of the chosen CP language. This paper addresses the core question of facilitating the third option, and as a side effect often makes the first two options unnecessary. The user-level extensibility of CP languages has been an important goal for over a decade. In the traditional global search approach to CP (namely heuristic-based tree search interleaved with propagation), higher-level abstractions for describing new constraints include indexicals [17]; (possibly enriched) deterministic finite automata (DFAs) via the automaton [2] and regular [11] generic constraints; and multivalued decision diagrams (MDDs) via the mdd [5] generic constraint. Usually, a generic but efficient propagation algorithm achieves a suitable level of local consistency by processing the higher-level description of the new constraint.

In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach.

Temporally expressive planning, an important class of temporal planning, has attracted much attention lately. Temporally expressive planning is difficult; few existing planners can solve them, as they have highly concurrent actions. We propose an optimal approach to temporally expressive planning based on a SAT formulation of the problem, finding solutions with the shortest time spans. Our experiments on several temporally expressive domains showed that our planner is able to optimally solve many instances in a reasonable amount of time, comparing favorably to existing temporally expressive planners. Our second result is a temporally expressive planning problem formulation of the Peer-to-Peer (P2P) network communications. In addition to demonstrating a better performance of our new method than the only existing temporally expressive planners on several temporally expressive problem domains, we apply our new planner to find optimal communication schedules for P2P networks. Our results will be potentially useful for designing efficient communication protocols in P2P networks.

The ability to automatically compose Web Services is critical for realising  more complex functionalities.  Several proposals to use automated planning to deal with the problem of service composition have been recently made. We present an approach, based on modelling the problem as a CSP (Constraint  Satisfaction Problem), that accommodates for the use of numeric variables, sensing and incomplete knowledge. We introduce a language for expressing extended goals, equipped with temporal constructs, maintainability properties, and an explicit distinction between sensing and achievement goals, in order to avoid undesirable situations.

This paper presents multi-goal planning for an autonomous blasthole drill used in open pit mining operations. Given a blasthole pattern to be drilled and constraints on the vehicle's motion and orientation when drilling, we wish to compute the best order in which to drill the given pattern. Blasthole pattern drilling is an asymmetric Traveling Salesman Problem with precedence constraints specifying that some holes must be drilled before others. We wish to find the minimum cost tour according to criteria that minimize the distance travelled satisfying the precedence and vehicle motion constraints. We present an iterative method for solving the blasthole sequencing problem using the combination of a Genetic Algorithm and motion planning simulations that we use to determine the true cost of travel between any two holes.

This paper presents a generic anytime forward-search constraint-based algorithm for solving planning problems expressed in the CNT framework (Constraint Network on Timelines). It is generic because it allows many kinds of search to be covered, from complete tree search to greedy search. It is anytime because some parameter settings, together with domain-specific knowledge, allow high quality plans to be produced very quickly and to be further improved. It is forward because it systematically considers the decisions to be made in a chronological order. It is finally constraint-based because it is built on top of the CNT framework which is an extension of the CSP framework able to model discrete event dynamic systems and because it is implemented on top of the Choco constraint programming tool from which it inherits all the constraint handling machinery. Experimental comparisons are made in terms of quality profile with other domain-dependent and domain-independent planners.

This paper considers Just-In-Time Job-Shop Scheduling, in which each activity has an earliness and a tardiness cost with respect to a due date. It proposes a constraint programming approach, which includes a novel filtering algorithm and dedicated heuristics. The filtering algorithm uses a machine relaxation to produce a lower bound that can be obtained by solving a Just-In-Time Pert problem. It also includes pruning rules which update the variable bounds and detect precedence constraints. The paper presents experimental results which demonstrate the effectiveness of the approach over a wide range of benchmarks.

In this paper we extend dynamic controllability of temporally-flexible plans to temporally-flexible reactive programs.  We consider three reactive programming language constructs whose behavior depends on runtime observations; conditional execution, iteration, and exception handling. Temporally-flexible reactive programs are distinguished from temporally-flexible plans in that program execution is conditioned on the runtime state of the world.  In addition, exceptions are thrown and caught at runtime in response to violated timing constraints, and handled exceptions are considered successful program executions.  Dynamic controllability corresponds to a guarantee that a program will execute to completion, despite runtime constraint violations and uncertainty in runtime state.  An algorithm is developed which frames the dynamic controllability problem as an AND/OR search tree over possible program executions.  A key advantage of this approach is the ability to enumerate only a subset of possible program executions that guarantees dynamic controllability, framed as an AND/OR solution subtree.

The dispatcher uses the dispatchable form to quickly make dynamic scheduling decisions. As autonomous systems become more capable and common, However, developing flexible executives for plans with they will need to reason about complex tasks and robustly choices, has been more difficult. Kim, Williams, and execute plans in uncertain environments. In previous work, Abramson present an executive called Kirk, which uses a Williams et al. introduced the Reactive Model-Based Programming deliberative planning step to change the execution sequence Language (RMPL), which is designed to allow online (2001). Although their results show improvement engineers to simply and intuitively express the desired behavior over prior planning systems, the latency is still too high for of the system (2003). Then the agent's executive determines tightly coupled systems, for example robots working with the correct sequence of actions to accomplish this humans or walking robots with fast dynamics. Recently, behavior, relieving the programmer of explicitly coding that Shah and Williams extended the compiler and dispatcher logic. RMPL programs often involve temporal constraints model to Temporal Constraint Satisfaction Problems (TCwhich the executives must reason over. SPs), a type of temporal problems with choice, by compactly Kim, Williams, and Abramson previously developed recording the possible set of solutions and efficiently Temporal Plan Networks (TPNs) as a temporal constraint reasoning over the possible options (2008).