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

Interaction and interest management in a scripting language.

Interaction management is concerned with the protocols that govern interactive activities among multiple users or agents in networked collaborative environments. Interest management is concerned with the relevance-based data filtering in networked collaborative environments. The main objective of the former is to structure interactive activities according to the requirements of the application concerned, while the main objective of the latter is to provide secured data transmission of a subset of information relevant to each recipient. The research in these two important aspects of networked software has largely been carried out in specific application domains such as online meetings, online groupware and online games. This thesis is concerned with the design and implementation of high-level language constructs for interaction and interest management. The work that has been undertaken includes: an abstract study of interactive activities and data transmission in networked collaborative environments through a large number of variations of the noughts and crosses game; the design of a set of language constructs for specifying a variety of interaction protocols; the design of a set of language constructs for specifying secured data sharing with relevance-based filtering; the implementation of these language constructs in the form of a major extension of a scripting language JACIE (Java-based Authoring Language for Collaborative Interactive Environments); the development of two demonstration applications, namely e-leaming on Simulation of Network Trouble Shooting and online Bridge, using the extended JACIE for demonstrating the technical feasibility and usefulness of the design. These high-level language constructs support a class of complicated software features in networked collaborative applications, such as turn management, interaction timing, group formation, dynamic protocol changes, distributed data sharing, access control, authentication and information filtering. They enable programmers to implement such features in an intuitive manner without involving low-level system programming directly, which would otherwise require the knowledge and skills of experienced network programmers

Distributing the Control of a Temporal Network among Multiple Agents

Agents collaborating on a set of tasks subject to temporal constraints must coordinate their activities to ensure that all of the temporal constraints are ultimately satisfied. Simple Temporal Networks (STNs) can be used to concisely represent temporal constraints; however, most algorithms for manipulating such networks presume that a single agent controls the network. Although recent research considers the controllability of networks in which Nature independently controls some temporal intervals, it nonetheless presumes that a single agent controls the rest of the network. This paper makes the following contributions. First, it argues for STNs augmented to accommodate the real-time execution of tasks. Although borrowing from existing approaches, it differs by sharply distinguishing between constraints in the network and the distribution of control over that network. Second, it introduces a more general conception of distributing control of a temporal network, one that is able to accommodate not only networks partially controlled by Nature, but also networks controlled by multiple agents. Third, it construes an existing algorithm for partitioning temporal networks into independent subnetworks as an algorithm for distributing control of a temporal network among multiple agents, each agent having sole control over one subnetwork. It then presents a more general algorithm that allows one of the subnetworks to remain dependent on the rest, thereby enabling the overall network to be less constrained. Restrictions on the control of the dependent subnetwork, specified in terms of necessary and sufficient bounds, guarantee an effective distribution of control over the network