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<h2>Information Science Discussion Papers Series: 2009 Abstracts</h2>

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<h3><a name="#dp2009-01">2009/01: Tag based model for knowledge sharing in agent society</a></h3>
<h4>S. Savarimuthu, M. Purvis and M. Purvis</h4>

<p>In this paper we discuss a tag-based model that facilitates knowledge sharing in the context of agents playing the knowledge sharing game. Sharing the knowledge incurs a cost for the sharing agent, and thus non-sharing is the preferred option for selfish agents. Through agent-based simulations we show that knowledge sharing is possible even in the presence of non-sharing agents in the population. We also show that the performance of an agent society can be better when some agents bear the cost of sharing instead of the whole group sharing the cost.</p>

<p><strong>Keywords: </strong>cooperation,
altruism,
tags,
knowledge sharing,
multi-agent based simulation,
artificial society</p>

<p><a href="papers/dp2009-01.pdf">Download</a> (PDF, 424 KB)</p>

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<h3><a name="#dp2009-02">2009/02: A software framework for application development using ZigBee protocol</a></h3>
<h4>B.T.R. Savarimuthu, M. Bruce and M. Purvis</h4>

<p>The problem with the uptake of new technologies such as ZigBee is the lack of development environments that help in faster application software development. This paper describes a software framework for application development using ZigBee wireless protocol. The architecture is based on defining XML based design interfaces that represent the profiles of ZigBee nodes that are used in the application.</p>


<p><a href="papers/dp2009-02.pdf">Download</a> (PDF, 168 KB)</p>

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<h3><a name="#dp2009-03">2009/03: Automatic sapstain detection in processed timber through image feature analysis</a></h3>
<h4>J.D. Deng</h4>

<p>Sapstain is considered a defect that must be removed from processed wood. So far, research in automatic wood inspection systems has been mostly limited to dealing with knots. In this paper, we extract a number of colour and texture features from wood pictures. These features are then assessed using machine learning techniques via feature selection, visualization, and Þnally classiÞcation. Apart from average colour and colour opponents, texture features are also found to be useful in classifying sapstain. This implies a signiÞcant modiÞcation to the domain understanding that sapstain is mainly a discolourization effect. Preliminary results are presented, with satisfactory classiÞcation performance using only a few selected features. It is promising that a real world wood inspection system with the functionality of sapstain detection can be developed.</p>


<p><a href="papers/dp2009-03.pdf">Download</a> (PDF, 884 KB)</p>