Decision-theoretic control of EUVE telescope scheduling

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems

Improving the Search Capabilities of a CFLP(FD) System

The CFLP system TOY(FD) is implemented in SICStus Prolog, and supports FD constraints by interfacing the CP(FD) solvers of Gecode and ILOG Solver. In this paper TOY(FD) is extended with new search primitives, in a setting easily adaptable to other Prolog CLP or CFLP systems. The primitives are described from a solver-independent point of view, pointing out some novel concepts not directly available in the Gecode and ILOG Solver libraries, as well as how to specify some search criteria at TOY(FD) level and how easily these strategies can be combined to set different search scenarios. The implementation of the primitives is described, presenting an abstract view of the requirements and how they are targeted to the Gecode and ILOG libraries. Finally, some benchmarks show that the new search strategies improve the solving performance of TOY(FD)

PrologPF: Parallel Logic and Functions on the Delphi Machine

PrologPF is a parallelising compiler targeting a distributed system of general purpose workstations connected by a relatively low performance network. The source language extends standard Prolog with the integration of higher-order functions. The execution of a compiled PrologPF program proceeds in a similar manner to standard Prolog, but uses oracles in one of two modes. An oracle represents the sequence of clauses used to reach a given point in the problem search tree, and the same PrologPF executable can be used to build oracles, or follow oracles previously generated. The parallelisation strategy used by PrologPF proceeds in two phases, which this research shows can be interleaved. An initial phase searches the problem tree to a limited depth, recording the discovered incomplete paths. In the second phase these paths are allocated to the available processors in the network. Each processor follows its assigned paths and fully searches the referenced subtree, sending solutions back to a control processor. This research investigates the use of the technique with a one-time partitioning of the problem and no further scheduling communication, and with the recursive application of the partitioning technique to effect dynamic work reassignment. For a problem requiring all solutions to be found, execution completes when all the distributed processors have completed the search of their assigned subtrees. If one solution is required, the execution of all the path processors is terminated when the control processor receives the first solution. The presence of the extra-logical Prolog predicate cut in the user program conflicts with the use of oracles to represent valid open subtrees. PrologPF promotes the use of higher-order functional programming as an alternative to the use of cut. The combined language shows that functional support can be added as a consistent extension to standard Prolog

Evaluation of the New York City Department of Youth and Community Development Out-of-School Time Programs for Youth Initiative: Implementation of Programs for High School Youth

Evaluates the Out-of-School Time Programs for Youth initiative's academic enhancement and recreational programs for high school youth, including enrollment, staff expertise, age-appropriate activities, and program partnerships to increase resources

Monadic constraint programming

A constraint programming system combines two essential components: a constraint solver and a search engine. The constraint solver reasons about satisfiability of conjunctions of constraints, and the search engine controls the search for solutions by iteratively exploring a disjunctive search tree defined by the constraint program. In this paper we give a monadic definition of constraint programming in which the solver is defined as a monad threaded through the monadic search tree. We are then able to define search and search strategies as first-class objects that can themselves be built or extended by composable search transformers. Search transformers give a powerful and unifying approach to viewing search in constraint programming, and the resulting constraint programming system is first class and extremely flexible

GLB: Lifeline-based Global Load Balancing library in X10

We present GLB, a programming model and an associated implementation that can handle a wide range of irregular paral- lel programming problems running over large-scale distributed systems. GLB is applicable both to problems that are easily load-balanced via static scheduling and to problems that are hard to statically load balance. GLB hides the intricate syn- chronizations (e.g., inter-node communication, initialization and startup, load balancing, termination and result collection) from the users. GLB internally uses a version of the lifeline graph based work-stealing algorithm proposed by Saraswat et al. Users of GLB are simply required to write several pieces of sequential code that comply with the GLB interface. GLB then schedules and orchestrates the parallel execution of the code correctly and efficiently at scale. We have applied GLB to two representative benchmarks: Betweenness Centrality (BC) and Unbalanced Tree Search (UTS). Among them, BC can be statically load-balanced whereas UTS cannot. In either case, GLB scales well-- achieving nearly linear speedup on different computer architectures (Power, Blue Gene/Q, and K) -- up to 16K cores

Hybridization of Bat and Genetic Algorithm to Solve N-Queens Problem

In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions