By Frans Coenen, Miltos Petridis
The papers during this quantity are the refereed technical papers provided at AI-2008, the Twenty-eighth SGAI foreign convention on leading edge innovations and purposes of synthetic Intelligence, held in Cambridge in December 2008.
They current new and leading edge advancements within the box, divided into sections on CBR and type, AI thoughts, Argumentation and Negotiation, clever platforms, From desktop studying To E-Learning and determination Making. the quantity additionally contains the textual content of brief papers provided as posters on the conference.
This is the twenty-fifth quantity within the examine and improvement sequence. The sequence is vital interpreting in case you desire to sustain so far with advancements during this very important field.
The software circulation papers are released as a spouse quantity less than the identify functions and strategies in clever structures XVI.
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Extra resources for Research and Development in Intelligent Systems XXV: Proceedings of AI-2008, The Twenty-eighth SGAI International Conference on Innovative Techniques ... of Artificial Intelligence
Example text
By showing the exact position of the snippet in the source file. The eclipse marker framework was used in order to avoid modifying source files. , if the snippet is of very generic nature, it might suffice to only store the code snippet and cut the link to the source file. Since we are also supporting the exchange of cases with other users of work group, there is also the option to publish the snippet on a coTag server. But this is not discussed any further in this paper. , for further annotating the code fragment.
1997). Bayesian Network Classifiers. Machine Learning, Vol. 29, pp 131-163. Kluwer Academic Publishers, Boston. Garg, A. and Roth, D. (2001) Understanding Probabilistic Classifiers. Proc. 12th European Conference on Machine Learning. Grossman, D. and Domingos, P. (2004). Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood. Proc. 21st International Conference on Machine Learning. , Geiger, D. M. (1995). Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
Creation date enables filtering cases from a certain time and provides provenance information. Finally, quality feedback allows for maintaining additional quality related information about the case. The tags’ similarity measure is used for determining a case’s global similarity value. The context attributes are used for filtering functionality to constrain the search space. Although, it could be reasonable to define similarities between different projects, languages or maybe even authors, the user interface would inevitably become more complicated.