30203063
Discourse model for collaborative design
Abstract A Discourse Model, including a structure and a process, is developed that provides software support for collaborative engineering design. The model shares characteristics of other design systems in the literature, including frames, constraints, semantic networks, and libraries of sharable design objects. It contributes a new model for conflict-aware agents, dynamic identification and dissemination of agent interest sets, a virtual workspace language, automatic detection of conflict, and a unique protocol for negotiation that ensures that interested agents have an opportunity to participate. The model is implementation independent and applicable to many research and commercial design environments currently available. An example scenario is provided in the architecture/engineering/construction domain that illustrates collaboration during the conceptual design of a fire station.
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.
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.
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>>
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.
23906830
PEAS: a robust energy conserving protocol for long-lived sensor networks
Small, inexpensive sensors with limited memory, computing power and short battery lifetimes are turning into reality. Due to adverse conditions such as high noise levels, extreme humidity or temperatures, or even destructions from unfriendly entities, sensor node failures may become norms rather than exceptions in real environments. To be practical, sensor networks must last for much longer times than that of individual nodes, and have yet to be robust against potentially frequent node failures. This paper presents the design of PEAS, a simple protocol that can build a long-lived sensor network and maintain robust operations using large quantities of economical, short-lived sensor nodes. PEAS extends system functioning time by keeping only a necessary set of sensors working and putting the rest into sleep mode. Sleeping ones wake up now and then, probing the local environment and replacing failed ones. The sleeping periods are self-adjusted dynamically, so as to keep the sensors' wakeup rate roughly constant, thus adapting to high node densities.
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
527997
Statistical en-route filtering of injected false data in sensor networks
In a large-scale sensor network individual sensors are subject to security compromises. A compromised node can inject into the network large quantities of bogus sensing reports which, if undetected, would be forwarded to the data collection point (i.e. the sink). Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. We present a statistical en-route filtering (SEF) mechanism that can detect and drop such false reports. SEF requires that each sensing report be validated by multiple keyed message authentication codes (MACs), each generated by a node that detects the same event. As the report is forwarded, each node along the way verifies the correctness of the MACs probabilistically and drops those with invalid MACs at earliest points. The sink further filters out remaining false reports that escape the en-route filtering. SEF exploits the network scale to determine the truthfulness of each report through collective decision-making by multiple detecting nodes and collective false-report-detection by multiple forwarding nodes. Our analysis and simulations show that, with an overhead of 14 bytes per report, SEF is able to drop 80/spl sim/90% injected false reports by a compromised node within 10 forwarding hops, and reduce energy consumption by 50% or more in many cases.
7682350
Incremental genetic K-means algorithm and its application in gene expression data analysis
BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
13148267
Fair scheduling in wireless packet networks
Fair scheduling of delay and rate-sensitive packet flows over a wireless channel is not addressed effectively by most contemporary wireline fair scheduling algorithms because of two unique characteristics of wireless media: (a) bursty channel errors, and (b) location-dependent channel capacity and errors. Besides, in packet cellular networks, the base station typically performs the task of packet scheduling for both downlink and uplink flows in a cell; however a base station has only a limited knowledge of the arrival processes of uplink flows.In this paper, we propose a new model for wireless fair scheduling based on an adaptation of fluid fair queueing to handle location-dependent error bursts. We describe an ideal wireless fair scheduling algorithm which provides a packetized implementation of the fluid model while assuming full knowledge of the current channel conditions. For this algorithm, we derive the worst-case throughput and delay bounds. Finally, we describe a practical wireless scheduling algorithm which approximates the ideal algorithm. Through simulations, we show that the algorithm achieves the desirable properties identified in the wireless fluid fair queueing model.