Learning for design reuse

Over the past decade 'design assistance', i.e. where the computer is viewed as an Intelligent Design Assistant (IDA) [MacCallum-etal85], has emerged in knowledge based design support and has formed the basic research strategy for the CAD Centre, University of Strathclyde, since the mid-80s. Within this philosophy, an IDA would act as a colleague to a designer, providing guidance, learning from past design experiences, carrying out semi and fully automated tasks, explaining its reasoning and in essence complementing the designer's own natural skills, and thus leaving the ultimate decision making, control and responsibility with the designer

Cruise Report 72-KB-23: Inshore fisheries habitat evaluation and monitoring program

Document has 1 page

Cruise Report 73-KB-12: Diving Safety Board

(1p.

The status of the California spiny lobster resource

California spiny lobsters, Panulirus interruptus, are the object of intense fisheries prosecuted by both commercial and sport fishermen in California. Recent evidence indicates that the resource may be declining, and an investigation of the population dynamics is needed. It is recommended that the investigation assess the magnitude of the fishery, identify population units and investigate the adequacy of existing gear regulations. (14pp.

Design reuse research : a computational perspective

This paper gives an overview of some computer based systems that focus on supporting engineering design reuse. Design reuse is considered here to reflect the utilisation of any knowledge gained from a design activity and not just past designs of artefacts. A design reuse process model, containing three main processes and six knowledge components, is used as a basis to identify the main areas of contribution from the systems. From this it can be concluded that while reuse libraries and design by reuse has received most attention, design for reuse, domain exploration and five of the other knowledge components lack research effort

Particle Acceleration in Gamma-Ray Burst Jets

Gradual shear acceleration of energetic particles in gamma-ray burst (GRB) jets is considered. Special emphasis is given to the analysis of universal structured jets, and characteristic acceleration timescales are determined for a power-law and a Gaussian evolution of the bulk flow Lorentz factor $\gamma_b$ with angle $\phi$ from the jet axis. The results suggest that local power-law particle distributions may be generated and that higher energy particles are generally concentrated closer to the jet axis. Taking several constraints into account we show that efficient electron acceleration in gradual shear flows, with maximum particle energy successively decreasing with time, may be possible on scales larger than $r \sim 10^{15}$ cm, provided the jet magnetic field becomes sufficiently weak and/or decreases rapidly enough with distance, while efficient acceleration of protons to ultra-high energies $> 10^{20}$ eV may be possible under a wide range of conditions.Comment: 11 pages, 3 figures; ApJ Letters accepted; final version: small typos corrected in order to match published versio

Internet Auctions with Artificial Adaptive Agents: A Study on Market Design

Many internet auction sites implement ascending-bid, second-price auctions. Empirically, lastminute or “late” bidding is frequently observed in “hard-close” but not in “soft-close” versions of these auctions. In this paper, we introduce an independent private-value repeated internet auction model to explain this observed difference in bidding behavior. We use finite automata to model the repeated auction strategies. We report results from simulations involving populations of artificial bidders who update their strategies via a genetic algorithm. We show that our model can deliver late or early bidding behavior, depending on the auction closing rule in accordance with the empirical evidence. As an interesting result, we observe that hard-close auctions raise less revenue than soft-close auctions. We also investigate interesting properties of the evolving strategies and arrive at some conclusions regarding both auction designs from a market design point of view.

Asset Price Bubbles and Crashes with Near-Zero-Intelligence Traders: Towards an Understanding of Laboratory Findings

We examine whether a simple agent--based model can generate asset price bubbles and crashes of the type observed in a series of laboratory asset market experiments beginning with the work of Smith, Suchanek and Williams (1988). We follow the methodology of Gode and Sunder (1993, 1997) and examine the outcomes that obtain when populations of zero-- intelligence (ZI) budget constrained, artificial agents are placed in the various laboratory market environments that have given rise to price bubbles. We have to put more structure on the behavior of the ZI-agents in order to address features of the laboratory asset bubble environment. We show that our model of "near--zero--intelligence" traders, operating in the same double auction environments used in several different laboratory studies, generates asset price bubbles and crashes comparable to those observed in laboratory experiments and can also match other, more subtle features of the experimental data.