9604157
Conflict resolution in cooperative design
Design is increasingly becoming a cooperative endeavor carried out by multiple agents with diverse kinds of expertise. The development of tools and underlying theories for supporting cooperative design has lagged, however, behind the growing needs implied by this evolution. 
The goal of this thesis work is to provide a computational model for supporting a critical component of cooperative design; the run-time resolution of conflicts. Though the long-term goal is to support unrestricted conflict resolution among human agents, this thesis focuses on an important subset of this problem: the resolution of domain-level conflicts in routine design, using machine-based agents. 
This thesis proposes a model of conflict resolution in cooperative design that corresponds well to how human design experts actually perform cooperative design, based on analysis of their group problem solving activity. This model supports natural participation for human experts, both at development-time as domain experts asked to create machine-based agents, as well as at run-time as problem solving agents themselves. The conflict resolution model is elaborated into a detailed implemented computational theory that offers many insights into how the conflict resolution expert and design agents should operate. The model incorporates a considerable body of conflict resolution expertise applicable to a wide variety of domains, as well as techniques for acquiring further expertise and evaluating its completeness.
35812356
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems
Abstract A knowledge-based learning system is developed to demonstrate that intelligently selecting a subset of examples based on domain knowledge for rule induction can be more productive than using all provided examples. Knowledge-based learning uses an incomplete domain theory and examples to induce knowledge missing in the theory. Knowledge-based learning systems determine where knowledge is missing in a domain theory, select a relevant subset of examples to use in induction, and induce new rules or modify existing rules.
5343942
Knowledge-Based Equation Discovery in Engineering Domains
Machine learning techniques that can compress datasets into relationships that underlie the data have great utility for engineers. Most model discovery systems are not well suited to discovering comprehensible models in engineering domains. In this paper we present KEDS, a Knowledge-based Equation Discovery System, which uses a model-driven approach to discover equations and thereby build models from engineering data.
6309018
Knowledge-based learning integrating acquisition and learning
Empirical learning algorithms are hampered by their inability to use domain knowledge to guide the induction of new rules. This paper describes knowledge-based learning, an approach to learning that selects the examples and relevant attributes for an empirical algorithm. Knowledge-based learning can be used for developing rules for engineering expert systems. Engineers often have some rules for problem solving, but also many experiences (examples) that facilitate solving problems. Knowledge-based learning systems are able to use both forms of knowledge.
12858200
Building Robust Learning Systems by Combining Induction and Optimization
Each concept description language and search strategy has an inherent inductive bias, a preference for some hypotheses over others. No single inductive bias performs optimally on all problems. This paper describes a system that couples induction with optimization to carry out an efficient search of large regions of inductive bias space. Experimental results are reported demonstrating the system's capacity to choose optimal biases even for complex and noisy problems.
37627056
SWIFT: system workbench for integrating and facilitating teams
There have been many R&D efforts in recent years to develop computer environments that directly enhance group problem-solving productivity. An example of such efforts is the computer-based virtual teaming technology (VTT) developed at the knowledge-based Engineering Sytems Research Laboratory (KESRL). The core software environment for KESRL's VTT is called SWIFT and is based on the knowledge processing technology (KPT) previously developed at KESRL to support various engineering design tasks. SWIFT is a flexible environment which enables the rapid development of collaborative applications, linking workers in distributed environments, of many domains. The paper presents the overall system architecture of SWIFT, and introduces the current and future R&D efforts at KESRL in realizing SWIFT for significant industrial applications.<<ETX>>
12609380
A topology-independent fair queueing model in ad hoc wireless networks
Fair queueing of rate and delay-sensitive packet flows in a shared-medium, multihop wireless network remains largely unaddressed because of the unique design issues such as location-dependent contention, spatial channel reuse, conflicts between ensuring fairness and maximizing channel utilization, and distributed fair scheduling. In this paper we propose a new topology-independent fair queueing model for a shared-medium ad hoc network. Our model ensures coordinated fair channel access among spatially contending flows while seeking to maximize spatial channel reuse. We describe packetized algorithms that realize the fluid fairness model with analytically provable performance bounds. We further design distributed implementations that approximate the ideal centralized algorithm. We evaluate our design through both simulations and analysis.
42944978
Reliability enhancement of electronic packages by design of optimal parameters
Abstract Reliability growth testing involves the selection of optimal design parameters to enhance a product's reliability. This paper proposes a split plot experimental design that accommodates the restriction on randomization on the order of experimental runs caused by the experimental nature of accelerated reliability testing. The proposed experimental design provides statistically relevant solutions about the choice of design parameters, in terms of their reliability impact, in a much shorter time. A degradation model that aids in predicting the failure time for the given problem further supplements this discussion.
30892527
On Design and Implementation of Adaptive Data Classification Scheme for DSM Systems
Distributed Shared Memory (DSM) environment is built by using specific softwares, to combine a number of computer hardware resources into one computing environment. Such environment not only provides an easy way to execute parallel applications, but also combines resources to speedup execution of these applications. DSM systems need to maintain data consistency in memory, what usually leads to communication overhead. Therefore, there exist a number of strategies that can be used to overcome this overhead and improve overall performance. Prefetching strategies have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. However, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the analysis of data access pattern during execution of parallel applications. We propose an Adaptive Data Classification scheme to improve prefetching strategy, with the goal to improve overall performance. Adaptive Data Classification scheme classifies data according to the access behavior of pages, so that home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, our method can improve the performance of prefetching strategies in DSM systems.
44527180
A Robust Data Delivery Protocol for Large Scale Sensor Networks
Recent technology advances in low-cost, low-power chip designs have made feasible the deployment of large-scale sensor networks. Although data forwarding has been among the first set of issues explored in sensor networking, how to reliably deliver sensing data through a vast field of small, vulnerable sensors remains a research challenge. In this paper we present GRAdient Broadcast (GRAB), a new set of mechanisms and protocols which is designed specifically for robust data delivery in spite of unreliable nodes and fallible wireless links. Similar to previous work [1], GRAB builds and maintains a cost field, providing each sensor in the network the direction to forward sensing data. Different from all the existing approaches, however, GRAB forwards data along an interleaved mesh from each source to the receiver. The width of the forwarding mesh is controlled by the amount of credit carried in each data message, allowing the degree of delivery robustness to be adjusted by the sender. GRAB design harnesses the advantage of large scale and relies on the collective efforts of multiple nodes to deliver data, without dependency on any individual ones. As demonstrated in our extensive simulation experiments, GRAB can successfully deliver above 90% of data with relatively low energy cost even under adverse conditions of up to 30% node failures compounded with 15% link packet losses.
18131628
Automatic workflow verification and generation
Correctness is an important aspect of workflow management systems. However, most of the workflow literature focuses only on the modeling aspects and assumes that a workflow is correct if during the execution it respects the control and data dependency specified by the workflow designer. To address the correctness question properly we propose a new workflow model based on Hoare semantics that allows to: (1) automatically check if the desired outcome of a workflow can be produced by its actual implementation, (2) automatically synthesize a workflow implementation from the workflow specification and a given task library.In particular we: (1) formalize the semantics of workflows and tasks with pre-and postconditions, (2) for each control construct we provide a set of sound inference rules formalizing its semantics. While most of our workflow constructs are standard, two of them are new: the universal and the existential constructs. We then describe algorithms for automatically checking the correctness of workflows and for automatic workflow generation.