5374807
Automated Planning and Scheduling using Calculus of Variations in Discrete Space
In this paper, we propose new dominance relations that can speed up significantly the solution process of planning problems formulated as nonlinear constrained dynamic optimization in discrete time and space. We first show that path dominance in dynamic programming cannot be applied when there are general constraints that span across multiple stages, and that node dominance, in the form of Euler-Lagrange conditions developed in optimal control theory in continuous space, cannot be extended to that in discrete space. This paper is the first to propose efficient node-dominance relations, in the form of local saddle-point conditions in each stage of a discrete-space planning problem, for pruning states that will not lead to locally optimal paths. By utilizing these dominance relations, we present efficient search algorithms whose complexity, despite exponential, has a much smaller base as compared to that without using the relations. Finally, we demonstrate the performance of our approach by integrating it in the ASPEN planner and show significant improvements in CPU time and solution quality on some spacecraft scheduling and planning benchmarks.
10007833
Constraint Partitioning for Solving Planning Problems with Trajectory Constraints and Goal Preferences
The PDDL3 specifications include soft goals and trajectory constraints for distinguishing highquality plans among the many feasible plans in a solution space. To reduce the complexity of solving a large PDDL3 planning problem, constraint partitioning can be used to decompose its constraints into subproblems of much lower complexity. However, constraint locality due to soft goals and trajectory constraints cannot be effectively exploited by existing subgoal-partitioning techniques developed for solving PDDL2.2 problems. In this paper, we present an improved partition-andresolve strategy for supporting the new features in PDDL3. We evaluate techniques for resolving violated global constraints, optimizing goal preferences, and achieving subgoals in a multivalued representation. Empirical results on the 5th International Planning Competition (IPC5) benchmarks show that our approach is effective and significantly outperforms other competing planners.
14272034
Optimal Anytime Constrained Simulated Annealing for Constrained Global Optimization
In this paper we propose an optimal anytime version of constrained simulated annealing (CSA) for solving constrained nonlinear programming problems (NLPs). One of the goals of the algorithm is to generate feasible solutions of certain prescribed quality using an average time of the same order of magnitude as that spent by the original CSA with an optimal cooling schedule in generating a solution of similar quality. Here, an optimal cooling schedule is one that leads to the shortest average total number of probes when the original CSA with the optimal schedule is run multiple times until it finds a solution. Our second goal is to design an anytime version of CSA that generates gradually improving feasible solutions as more time is spent, eventually finding a constrained global minimum (CGM). In our study, we have observed a monotonically non-decreasing function relating the success probability of obtaining a solution and the average completion time of CSA, and an exponential function relating the objective target that CSA is looking for and the average completion time. Based on these observations, we have designed CSAAT-ID the anytime CSA with iterative deepening that schedules multiple runs of CSA using a set of increasing cooling schedules and a set of improving objective targets. We then prove the optimality of our schedules and demonstrate experimentally the results on four continuous constrained NLPs. CSAAT-ID can be generalized to solving discrete, continuous, and mixed-integer NLPs, since CSA is applicable to solve problems in these three classes. Our approach can also be generalized to other stochastic search algorithms, such as genetic algorithms, and be used to determine the optimal time for each run of such algorithms.
12984676
Hybrid Evolutionary And Annealing Algorithms For Nonlinear Discrete Constrained Optimization
This paper presents a procedural framework that unifies various mechanisms to look for discrete-neighborhood saddle points in solving discrete constrained optimization problems (DCOPs). Our approach is based on the necessary and sufficient condition on local optimality in discrete space, which shows the one-to-one correspondence between the discrete-space constrained local minima of a problem and the saddle points of the corresponding Lagrangian function. To look for such saddle points, we study various mechanisms for performing ascents of the Lagrangian function in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our results show that CSAEA, a combined constrained simulated annealing and evolutionary algorithm, performs well when using mutations and crossovers to generate trial points and accepting them based on the Metropolis probability. We apply iterative deepening to determine the optimal number of generations in CSAEA and show that its performance is robust with respect to changes in population size. To test the performance of the procedures developed, we apply them to solve some continuous and mixed-integer nonlinear programming (NLP) benchmarks and show that they obtain better results than those of existing algorithms.
832740
Constrained genetic algorithms and their applications in nonlinear constrained optimization
The paper presents a problem-independent framework that unifies various mechanisms for solving discrete constrained nonlinear programming (NLP) problems whose functions are not necessarily differentiable and continuous. The framework is based on the first-order necessary and sufficient conditions in the theory of discrete constrained optimization using Lagrange multipliers. It implements the search for discrete-neighborhood saddle points (SP/sub dn/) by performing ascents in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our study on the various mechanisms shows that CSAGA, a combined constrained simulated annealing and genetic algorithm, performs well. Finally, we apply iterative deepening to determine the optimal number of generations in CSAGA.
12806220
Temporal Planning using Subgoal Partitioning and Resolution in SGPlan
In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong locality of mutex constraints observed in many benchmarks of the Fourth International Planning Competition (IPC4), we propose to partition the constraints of a planning problem into groups based on their subgoals. Constraint partitioning leads to significantly easier subproblems that are similar to the original problem and that can be efficiently solved by the same planner with some modifications to its objective function. We present a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subproblems and that resolves those inconsistent global constraints across the subproblems. We also discuss some implementation details of SGPlan4, which include the resolution of violated global constraints, techniques for handling producible resources, landmark analysis, path finding and optimization, search-space reduction, and modifications of Metric-FF when used as a basic planner in SGPlan4. Last, we show results on the sensitivity of each of these techniques in quality-time trade-offs and experimentally demonstrate that SGPlan4 is effective for solving the IPC3 and IPC4 benchmarks.
41090805
Distributed policy specification and enforcement in service-oriented business systems
Service-oriented computing (SOC) and Web services (WS) provide a flexible computing platform for electronic business and commerce. Introducing policy-based computing to service-oriented business systems adds another dimension of flexibility and security. While service composition and re-composition in service-oriented business systems allow major system reconstruction, policy-based computing can better deal with the small and routine changes of business processing. This paper reports our latest research on integrating policy-based computing into service-oriented business system and discusses its feasibility, benefits and cost. Under this research, we designed a policy specification and enforcement language PSEL for specifying system constraints and business rules. We implemented a runtime environment in which a service-oriented business system can be modeled, analyzed, deployed, and executed with policy enforcement. Automated tools have been developed to facilitate the entire development process. The cost of policy-based computing is experimentally evaluated
39358979
Multi-agent based dynamic scheduling for a flexible assembly system
This paper proposes a multi-agent based dynamic scheduling approach for a flexible assembly system. We first introduce a flexible control system developed by Intelligent Robotics and Automation Laboratory in National Taiwan University. Based on that control system, the agents can communicate with each other conveniently. A generic agent architecture is proposed to model the pieces of equipment in the flexible assembly system. With a distributed architecture, the agents make their scheduling decisions using their local rule base. The agents acquire the resources following the distributed resource allocation protocol. The scheduling complexity is reduced to meet the real-time response requirement in the applications for flexible automated production. The present work is applied to the experimental robotized flexible assembly system in the above laboratory.
5924956
On joining and caching stochastic streams
We consider the problem of joining data streams using limited cache memory, with the goal of producing as many result tuples as possible from the cache. Many cache replacement heuristics have been proposed in the past. Their performance often relies on implicit assumptions about the input streams, e.g., that the join attribute values follow a relatively stationary distribution. However, in general and in practice, streams often exhibit more complex behaviors, such as increasing trends and random walks, rendering these "hardwired" heuristics inadequate.In this paper, we propose a framework that is able to exploit known or observed statistical properties of input streams to make cache replacement decisions aimed at maximizing the expected number of result tuples. To illustrate the complexity of the solution space, we show that even an algorithm that considers, at every time step, all possible sequences of future replacement decisions may not be optimal. We then identify a condition between two candidate tuples under which an optimal algorithm would always choose one tuple over the other to replace. We develop a heuristic that behaves consistently with an optimal algorithm whenever this condition is satisfied. We show through experiments that our heuristic outperforms previous ones.As another evidence of the generality of our framework, we show that the classic caching/paging problem for static objects can be reduced to a stream join problem and analyzed under our framework, yielding results that agree with or extend classic ones.
14759214
Real-coded ECGA for economic dispatch
In this paper, we propose a new approach that consists of the extended compact genetic algorithm (ECGA) and split-on-demand (SoD), an adaptive discretization technique, to economic dispatch (ED) problems with nonsmooth cost functions. ECGA is designed for handling problems with decision variables of the discrete type, while the decision variables of ED problems are oftentimes real numbers. Thus, in order to employ ECGA to tackle ED problems, SoD is utilized for discretizing the continuous decision variables and works as the interface between ECGA and the ED problem. Furthermore, ED problems in practice are usually hard for traditional mathematical programming methodologies because of the equality and inequality constraints. Hence, in addition to integrating ECGA and SoD, in this study, we devise a repair operator specifically for making the infeasible solutions to satisfy the equality constraint. To examine the performance and effectiveness, we apply the proposed framework to two different-sized ED problems with nonsmooth cost function considering the valve-point effects. The experimental results are compared to those obtained by various evolutionary algorithms and demonstrate that handling ED problems with the proposed framework is a promising research direction.
10353520
Finding Self-Similarities in Opportunistic People Networks
Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.