8669815
Guiding Agent Learning in Design
In this paper we discuss the need for learning in multi-agent design systems, and the variety of forms it might take. We propose a particular method of guiding learning in these systems, describe an architecture for its implementation, and discuss how the learning should be evaluated.
195710721
Special Issue: Machine Learning in Design
This issue of AIEDAM is based on a workshop on Machine Learning in Design held at the 1996 Conference on Artificial Intelligence in Design, AID'96 (Gero & Sudweeks, 1996), the third of such workshops, with the previous two being held at AID'92 (Gero, 1992) and at AID'94 (Gero & Sudweeks, 1994). The first two workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996).
6315058
Knowledge and Control for a Mechanical Design Expert System
A new variety of Oriental hybrid lily plant bearing large upright-facing flowers particularly characterized by their clear pink coloration with deeper midribs, their large size and substance, and their upright to semi-upright orientation, the flowers being of excellent form and long persistence both on the plant and as cut flowers. This combination is completely new in the Oriental hybrid divisions of lilies suited to forcing and to mass commercial cultivation. The plant is an excellent garden plant, highly resistant to disease and shows tolerance of virus; the bulbs may be precooled and forced for cut flower production and the clone is vigorous, a good grower and a good propagator.
7484630
A Graduate Level Expert Systems Course
This article presents an approach to a graduate-level course in expert, knowledge-based, problem-solving systems. The core of the course, and this article, is a set of questions called a profile, that can be used to characterize and compare each system studied.
14384022
Designing semantics-preserving cluster representatives for scientific input conditions
In scientific domains, knowledge is often discovered from experiments by grouping or clustering them based on the similarity of their output. The causes of similarity are analyzed based on the input conditions characterizing a given type of output, i.e., a given cluster. This analysis helps in applications such as decision support in industry. Cluster representatives form at-a-glance depictions for such applications. Randomly selecting a set of conditions in a cluster as its representative is not sufficient since distinct combinations of inputs could lead to the same cluster. In this paper, an approach called DesCond is proposed to design semantics-preserving cluster representatives for scientific input conditions. We define a notion of distance for conditions to capture semantics based on the types of their attributes and their relative importance. Using this distance, methods of building candidate cluster representatives with different levels of detail are proposed. Candidates are compared using the DesCond Encoding proposed in this paper that assesses their complexity and information loss, given user interests. The candidate with the lowest encoding for each cluster is returned as its designed representative. DesCond is evaluated with real data from Materials Science. Evaluation with domain expert interviews and formal user surveys shows that designed representatives consistently outperform randomly selected ones and different candidates suit different users.
13541175
Evaluating Network Serviceability
Abstract The capability to manage network operations on a day-to-day basis has become an important area of concern given the rapid expansion of computer networks. A related, but much less studied area, is the impact that the design of a network has on the ease with which it can be serviced. Evaluating the ease of servicing the network and its components does not concentrate on day-to-day operations of the network, but rather examines the network design over a longer term to identify problematic components, configurations and dependencies between components based on expert knowledge and historical data. This paper reports on our experience with a system to evaluate the ease of servicing a network—its serviceability—given a description of its design. The motivation for the work is that computer networks are more often “grown” than “designed” without concern for serviceability issues. The work also examines the impact of dependencies between components on the role of service and the potential disruption costs caused by these dependencies. This work is important because it is pro-active in explicitly considering the role of servicing a network when changing the components or configuration of a network. This approach is in contrast to network management, which is reactive in dealing with problems. The paper reports on a successful implementation of such as system and our experiences with it.
14054763
The use of functional domains to improve transmembrane protein topology prediction
Transmembrane proteins affect vital cellular functions and pathogenesis, and are a focus of drug design. It is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Computational tools for transmembrane protein topology prediction fill in the gap between the abundance of transmembrane proteins and the scarcity of known membrane protein structures. Their prediction accuracy is still inadequate: TMHMM, the current state-of-the-art method, has less than 52% accuracy in topology prediction on one set of transmembrane proteins of known topology. Based on the observation that there are functional domains that occur preferentially internal or external to the membrane, we have extended the model of TMHMM to incorporate functional domains, using a probabilistic approach originally developed for computational gene finding. Our extension is better than TMHMM in predicting the topology of transmembrane proteins. As prediction of functional domain improves, our system's prediction accuracy will likely improve as well.
34635988
Virtual Travel Training for People with Learning Disabilities Accessing Employment Including the Introduction to the Special Thematic Session "Virtual Reality"
The purpose of this project is to develop a Virtual Learning Environment to assist in the travel training of a group of people with learning disabilities to enable them to gain access to training and employment opportunities. To achieve this a realistic Virtual Travel in Sutton Package will be developed to assist people with special needs to learn independent travel skills, and to assess the effectiveness of the software in achieving this aim.
12974717
A System for Learning Routes and Schedules with Genetic Algorithms
18888101
Validation of Elliptic Curve Public Keys
We present practical and realistic attacks on some standardized elliptic curve key establishment and public-key encryption protocols that are effective if the receiver of an elliptic curve point does not check that the point lies on the appropriate elliptic curve. The attacks combine ideas from the small subgroup attack of Lim and Lee, and the differential fault attack of Biehl, Meyer and Muller. Although the ideas behind the attacks are quite elementary, and there are simple countermeasures known, the attacks can have drastic consequences if these countermeasures are not taken by implementors of the protocols. We illustrate the effectiveness of such attacks on a key agreement protocol recently proposed for the IEEE 802.15 Wireless Personal Area Network (WPAN) standard.
2693848
A parallel heuristic for quadratic assignment problems
Abstract The quadratic assignment problem represents an important class of problems with applications as diverse as facility layout and data analysis. The importance of these applications coupled with the fact that the quadratic assignment problem is NP-hard has encouraged the development of heuristics because optimal seeking procedures have been restricted to very small versions of the problem. This paper describes a new parallel heuristic, SAGA, for the quadratic assignment problem. SAGA is a cascaded hybrid of a genetic algorithm and simulated annealing. In addition to details regarding SAGA and its implementation, this paper also describes the performance of SAGA on two standard problems taken from the literature. The results from these problems show SAGA to be superior to the most commonly employed heuristic in solution quality, and for large problems it is also superior in solution time.