Multi-Attribute Reverse Auctions (MARAs) are excellent protocols to automate negotiation among sellers. Eliciting the buyer0s preferences and determining the winner are both challenging problems for MARAs. To solve these problems, we propose two algorithms namely MAUT* and CP-net*, which are respectively the improvement of the Multi-Attribute Utility Theory (MAUT) and constrained CP-net. The buyers can now express conditional, qualitative as well as quantitative preferences over the item attributes. To evaluate the performance in time of the proposed algorithms, we conduct an experimental study on several problem instances. The results favor MAUT* in most of the cases.

Emotions are an integral part of human decision making. It follows that emotions should take part in the decision process towards the design of more realistic artificial agents. Three psychological models for emotions are examined and a corresponding algorithm is developed for each depicting its process. A generalized multi-agent model is designed to demonstrate the implementation of each of the three methods. An agent thus represents a human capable of exhibiting emotional state in response to an arbitrary emotionally charged event of varying impact.

Thomas K. Landauer (Pearson Knowledge Technologies) The recently created word maturity (WM) metric uses the computational language model LSA to mimic the average evolutionary growth of individual word and paragraph knowledge as a function of the total amount and order of simulated reading. The simulator traces the separate growth trajectories of an unlimited number of different words from the beginning of reading to adult level.

To overcome the problems in Thai word segmentation, a number of word segmentation has been proposed during the long period of time until today. We propose a novel Thai word segmentation approach so called Co-occurrence-Based Error Correction (CBEC). CBEC generates all possible segmentation candidates using the classical maximal matching algorithm and then selects the most accurate segmentation based on co-occurrence and an error correction algorithm. CBEC was trained and evaluated on BEST 2009 corpus.

This study investigates Serious Games (SG) to assess motivational factors appropriate to an Intelligent Tutoring System (ITS). An ITS can benefit from SG’ elements that can highly support learners’ motivation. Thus, identifying and assessing the effect that these factors may have on learners is a crucial step before attempting to integrate them into an ITS. We designed an experiment using a Serious Game and combined both the theoretical ARCS model of motivation and empirical physiological sensors (heart rate, skin conductance and EEG) to assess the effects of motivational factors on learners. We then identified physiological patterns correlated with one motivational factor in a Serious Game (Alarm triggers) associated with the Attention category of the ARCS model. The best result of three classifiers run on the physiological data has reached an accuracy of 73.8% in identifying learners’ attention level as being either above or below average. These results open the door to the possibility for an ITS to discriminate between attentive and inattentive learners.

Expressions (MWEs) (Okita et al. 2010), this may improve the overall translation. For example in EN-JP, the empirical evidences 2007; Koehn 2010) intends to handle morphologically rich suggest that we separate affix(es) and word stem(s) since it languages in the target side by integrating additional linguistic obtains better BLEU score than the case when we do not separate markup at the word level, where each type of additional them although the adequacy decreases.

We present research in decision tree analysis that studies a data set and finds new patterns that were not obvious using statistical methods. Our method is applied to a database of accommodative esotropic patients. Accommodative esotropia is an eye disease that when left untreated leads to blindness. Patients whose muscles deteriorate often need corrective surgery, since less invasive methods of treatment tend to fail in these patients. Using a learn and prune methodology, decision tree analysis of 354 accommodative esotropic patients led to the discovery of two conjunctive variables that predicted deterioration in the initial year of treatment better than what was previously determined using standard statistical methods.

When people interact with language-producing agents (other people or computers), they assume that the shared experience leads to shared representations — of the world, the interaction, and the language used in the interaction. This phenomenon occurs even during interaction with systems that give no evidence of building shared representations. The absence of shared representations leads to errors and delays; alternatively, even simple shared representations can lead to reduced error rates and more efficient interaction. In this talk, we present three case studies: a mobile local business search application that builds no interaction representations; a telephone-based recommendation and review system that builds limited representations of the shared language in the interaction; and computer models of coreference that use shared representations to permit both coreference resolution and referring expression generation. We lay out a range of possibilities for shared representations, show that they can be built incrementally as an interaction progresses, and point to possibilities for future work in probabilistic shared representations for interactive systems.

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typically the case that most of the knowledge in the system is likely to be correct as specified by the expert(s) and/or knowledge engineer(s). In this paper, we propose a method to identify the possible changes by understanding the contribution of parameters on the outputs of concern. Our approach is based on Bayesian Knowledge Bases for modeling uncertainties. We start with single parameter changes and then extend to multiple parameters. In order to identify the optimal solution that can minimize the change to the model as specified by the domain experts, we define and evaluate the sensitivity values of the results with respect to the parameters. We discuss the computational complexities of determining the solution and show that the problem of multiple parameters changes can be transformed into Linear Programming problems, and thus, efficiently solvable. Our work can also be applied towards validating the knowledge base such that the updated model can satisfy all test-cases collected from the domain experts.

In recent years, there has been considerable interest in the use of planning techniques in the area of new media. Many traditional planning notions no longer apply in the context of these applications. In particular, it can be difficult to answer the important question of what constitutes a good plan for the domain, but there is an emerging consensus that plan dynamics play an important role. As a consequence, it is important to support representation of such aspects. Our solution is to introduce a meta-level of representation that is an abstraction of the domain with respect to both time and causality, and to develop a visual representation of this in the form of a narrative arc. This visual representation can then be used in a visual programming approach to the exploration and specification of plan dynamics. In the paper we outline this approach to meta-level representation using constraints along with the visual programming interface we have developed. We illustrate the approach with examples of visual programming in the development of an interactive entertainment system based on Shakespeare's play ``The Merchant of Venice''