Constrained Task Assignment and Scheduling on Networks of Arbitrary Topology.

This dissertation develops a framework to address centralized and distributed constrained task assignment and task scheduling problems. This framework is used to prove properties of these problems that can be exploited, develop effective solution algorithms, and to prove important properties such as correctness, completeness and optimality. The centralized task assignment and task scheduling problem treated here is expressed as a vehicle routing problem with the goal of optimizing mission time subject to mission constraints on task precedence and agent capability. The algorithm developed to solve this problem is able to coordinate vehicle (agent) timing for task completion. This class of problems is NP-hard and analytical guarantees on solution quality are often unavailable. This dissertation develops a technique for determining solution quality that can be used on a large class of problems and does not rely on traditional analytical guarantees. For distributed problems several agents must communicate to collectively solve a distributed task assignment and task scheduling problem. The distributed task assignment and task scheduling algorithms developed here allow for the optimization of constrained military missions in situations where the communication network may be incomplete and only locally known. Two problems are developed. The distributed task assignment problem incorporates communication constraints that must be satisfied; this is the Communication-Constrained Distributed Assignment Problem. A novel distributed assignment algorithm, the Stochastic Bidding Algorithm, solves this problem. The algorithm is correct, probabilistically complete, and has linear average-case time complexity. The distributed task scheduling problem addressed here is to minimize mission time subject to arbitrary predicate mission constraints; this is the Minimum-time Arbitrarily-constrained Distributed Scheduling Problem. The Optimal Distributed Non-sequential Backtracking Algorithm solves this problem. The algorithm is correct, complete, outputs time optimal schedules, and has low average-case time complexity. Separation of the task assignment and task scheduling problems is exploited here to ameliorate the effects of an incomplete communication network. The mission-modeling conditions that allow this and the benefits gained are discussed in detail. It is shown that the distributed task assignment and task scheduling algorithms developed here can operate concurrently and maintain their correctness, completeness, and optimality properties.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91527/1/jpjack_1.pd

The impact of the conflict on solving distributed constraint satisfaction problems

Distributed Constraint Satisfaction Problems (DCSPs) involve a vast number of AI andMulti-Agent problems. Many important efforts have been recen accomplished for solving these kinds of problems using both backtracking-based and mediation-based methods. One of the most successful mediation based algorithms in this field is Asynchronous Partial Overlay (APO) algorithm. By choosing some agents as mediators, APO tries to centralize portions of the distributed problem, and then each mediator tries to solve its centralized sub-problem. This work continues until the whole problem is solved. This paper presents a new strategy to select mediators. The main idea behind this strategy is that the number of mediators conflicts (violated constraints) impacts directly on its performance. Experimental results show that choosing the mediators with the most number of conflicts not only leads to considerable decrease in APO complexity, but also it can decrease the complexity of the other extensions of the APO such as IAPO algorithm. MaxCAPO and MaxCIAPO are two new expansions of APO which introduce this idea and are presented in this article. The results of using this mediator selection strategy show a rapid and desirable improvement over various parameters in comparison with APO and IAP

Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Hybrid algorithms for distributed constraint satisfaction.

A Distributed Constraint Satisfaction Problem (DisCSP) is a CSP which is divided into several inter-related complex local problems, each assigned to a different agent. Thus, each agent has knowledge of the variables and corresponding domains of its local problem together with the constraints relating its own variables (intra-agent constraints) and the constraints linking its local problem to other local problems (inter-agent constraints). DisCSPs have a variety of practical applications including, for example, meeting scheduling and sensor networks. Existing approaches to Distributed Constraint Satisfaction can be mainly classified into two families of algorithms: systematic search and local search. Systematic search algorithms are complete but may take exponential time. Local search algorithms often converge quicker to a solution for large problems but are incomplete. Problem solving could be improved through using hybrid algorithms combining the completeness of systematic search with the speed of local search. This thesis explores hybrid (systematic + local search) algorithms which cooperate to solve DisCSPs. Three new hybrid approaches which combine both systematic and local search for Distributed Constraint Satisfaction are presented: (i) DisHyb; (ii) Multi-Hyb and; (iii) Multi-HDCS. These approaches use distributed local search to gather information about difficult variables and best values in the problem. Distributed systematic search is run with a variable and value ordering determined by the knowledge learnt through local search. Two implementations of each of the three approaches are presented: (i) using penalties as the distributed local search strategy and; (ii) using breakout as the distributed local search strategy. The three approaches are evaluated on several problem classes. The empirical evaluation shows these distributed hybrid approaches to significantly outperform both systematic and local search DisCSP algorithms. DisHyb, Multi-Hyb and Multi-HDCS are shown to substantially speed-up distributed problem solving with distributed systematic search taking less time to run by using the information learnt by distributed local search. As a consequence, larger problems can now be solved in a more practical timeframe

Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques