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.
28843712
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts
This paper describes the Competitive Relation Learner (CRL), a generalized recursive splitting algorithm capable of producing a wide range of hybrid concept representations through the competitive application of multiple learning strategies, multiple decomposition strategies, and multiple decomposition evaluation strategies. Experimental results are reported that demonstrate CRL's ability to outperform several well known fixed-bias strategies.
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.
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.
12297349
Creating and using models for engineering design: a machine-learning approach
An adaptive and interactive modeling system (AIMS) that integrates simulation, optimization and machine learning to help engineers make design decisions is described. AIMS views engineering decision making as a two-phase process of creating and then using models. The competitive relation learner and the induce-and-select optimizer, AIMS's two main components, and their roles in both phases of decision-making are discussed. AIMS's role in supporting the design of a diesel engine that outputs power within the 440- to 460-kW range and consumes the least amount of fuel is also discussed.<<ETX>>
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>>
6607709
Itinerary-Based Access Control for Mobile Tasks in Scientific Workflows
Current scientific workflow models require datasets to be transferred from their source to the hosts where they can be processed. This seriously impedes data-intensive applications. In order to address this limitation, we propose a notion of mobile tasks that can move from their home hosts towards datasets and perform computation on the dataset side. Since a mobile task might migrate across various administrative domains and get executed at multiple hosts, it is critically important to ensure the security of a mobile- task-based workflow system. In this paper, we propose an itinerary-based access control model to ensure the secure migration of mobile tasks.
2709125
A two-tier data dissemination model for large-scale wireless sensor networks
Sink mobility brings new challenges to large-scale sensor networking. It suggests that information about each mobile sink's location be continuously propagated through the sensor field to keep all sensor nodes updated with the direction of forwarding future data reports. Unfortunately frequent location updates from multiple sinks can lead to both excessive drain of sensors' limited battery power supply and increased collisions in wireless transmissions. In this paper we describe TTDD, a Two-Tier Data Dissemination approach that provides scalable and efficient data delivery to multiple mobile sinks. Each data source in TTDD proactively builds a grid structure which enables mobile sinks to continuously receive data on the move by flooding queries within a local cell only. TTDD's design exploits the fact that sensor nodes are stationary and location-aware to construct and maintain the grid structures with low overhead. We have evaluated TTDD performance through both analysis and extensive simulation experiments. Our results show that TTDD handles multiple mobile sinks efficiently with performance comparable with that of stationary sinks.
5694991
Robust Packet Scheduling in Wireless Cellular Networks
This paper addresses the following robust scheduling problem: Given that only coarse-grained channel state information (i.e., bounds on channel errors, but not the fine-grained error pattern) is available, how to design a robust scheduler that ensures worst-case optimal performance? To solve this problem, we consider two coarse-grained channel error models and take a zero-sum game theoretic approach, in which the scheduler and the channel error act as non-cooperative adversaries in the scheduling process. Our results show that in the heavy channel error case, the optimal scheduler adopts a threshold form. It does not schedule a flow if the price (the flow is willing to pay) is too small, in order to maximize the system revenue. Among the scheduled flows, the scheduler schedules a flow with a probability inversely proportional to the flow price such that the risk of being caught by the channel error adversary is minimized. We also show that in the mild channel error model, the robust scheduling policy exhibits a balanced trade-off between a greedy decision and a conservative policy. The scheduler is likely to take a greedy decision if it evaluates the risk of encountering the channel error adversary now to be small. Therefore, robust scheduling does not always imply conservative decision. The scheduler is willing to take “risks” to expect higher gain in some scenarios. Our solution also shows that probabilistic scheduling may lead to higher worst-case performance compared to traditional deterministic policies. Finally, the current efforts show the feasibility to explore a probabilistic approach to cope with dynamic channel error conditions.
1137858
Runtime Security Verification for Itinerary-Driven Mobile Agents
We present a new approach to ensure the secure execution of itinerary-driven mobile agents, in which the specification of the navigational behavior of an agent is separated from the specification of its computational behavior. We empower each host with an access control policy so that the host will deny the access from an agent whose itinerary does not conform to the host's access control policy. A host uses model checking algorithms to check if the itinerary of the agent conforms to its access control policy written in mu-calculus, and if so, grant access permission. In order to address the state explosion problem for model checking itineraries, we propose an approach called model generation code. In this approach, instead of verifying the itinerary itself, a host actually checks the conservative models of a mobile agent. If a conservative model does not satisfy the host's access control policy, the mobile agent will provide refined models for further verification. Our preliminary results show that this is a practical and promising approach to ensure the secure execution of mobile agents
7275848
Contour maps: Monitoring and diagnosis in sensor networks
Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink. In this paper, we propose an efficient data-collection scheme that can be used for event monitoring or network-wide diagnosis. Our scheme relies on the well-known representation of data--contour maps, which trade off accuracy with the amount of samples. The scheme consists of three novel algorithms to build contour maps: distributed spatial and temporal data suppression, contour reconstruction at the sink via interpolation and smoothing, and an efficient mechanism to convey routing information over multiple hops. By reducing the number of transmissions required to convey relevant information to the sink, the proposed contour mapping scheme saves energy and improves network lifetime. In a sharp contrast to related work in this area, the scheme does not require all nodes to explicitly share information.The contour mapping scheme can be applied for tasks such as: (1) presenting a global picture of the network in both temporal and spatial domains, (2) being used as a diagnosis tool, e.g., to detect faulty sensors and to scan for residual energy, (3) working in concert with in-network aggregation schemes to further reduce the communication overhead of aggregation schemes. The proposed scheme imposes little processing and storage overhead, allowing for the sensor networking paradigm of 'dumb sensor, smart sink' which enables economical deployment of large-scale sensor networks.Simulation results show that our scheme is resilient to both high packet loss rate and measurement noise. The design is also energy efficient, resulting in up to an-order-of-magnitude power savings when compared with the base line scheme where every sensor sends its report to the sink.