Abstract. A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models. 1

It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly mitigates DTWs demanding CPU time has sparked a flurry of research activity. However, the technique and its many extensions still only allow DTW to be applied to moderately large datasets. In addition, almost all of the research on DTW has focused exclusively on speeding up its calculation; there has been little work done on improving its accuracy. In this work, we target the accuracy aspect of DTW performance and introduce a new framework that learns arbitrary constraints on the warping path of the DTW calculation. Apart from improving the accuracy of classification, our technique as a side effect speeds up DTW by a wide margin as well. We show the utility of our approach on datasets from diverse domains and demonstrate significant gains in accuracy and efficiency.

The go-playing program Honte is described. It uses neural nets together with more conventional AI-methods like alpha-beta search. A neural net is trained by supervised learning to imitate local shapes made in a database of expert games. A second net is trained to estimate the safety of groups by self play using TD(l)- learning. A third net is trained to estimate territorial potential of unoccupied points, also based on self play and TD(l)-learning. Although the program has not yet reached the level of the best commercial go-programs, results are encouraging. 1 INTRODUCTION This article describes the go-playing program Honte. The name "Honte" (pronounced hon-teh) means "proper", "sound" or "correct" in Japanese. The idea behind Honte is to use neural nets together with other programming techniques, hopefully getting the best of all worlds. Not all sections of the article are relevant to machine learning as such, but for a problem as complex as go, integration of different techniques i...

Background. As biologists increasingly rely upon computational tools, it is imperative that they be able to appropriately apply these tools and clearly understand the methods the tools employ. Such tools must have access to all the relevant data and knowledge and, in some sense, ‘‘understand’ ’ biology so that they can serve biologists ’ goals appropriately and ‘‘explain’ ’ in biological terms how results are computed. Methodology/Principal Findings. We describe a deduction-based approach to biocomputation that semiautomatically combines knowledge, software, and data to satisfy goals expressed in a high-level biological language. The approach is implemented in an open am worried about. that first-order-logic is bad t++done with the help of an automatic theorem prover equipped with an appropriatsource web-based biocomputing platform called BioDeducta, which combines SRI’s SNARK theorem prover with the BioBike interactive integrated knowledge base. The biologist/user expresses a high-level conjecture, representing a biocomputational goal query, without indicating how this goal is to be achieved. A subject domain theory, represented in SNARK’s logical language, transforms the terms in the conjecture into capabilities of the available resources and the background knowledge necessary to link them together. If the subject domain theory enables SNARK to prove the conjecture—that is, to find paths between the goal and BioBike resources—then the resulting proofs represent solutions to the conjecture/query. Such proofs provide provenance for each result, indicating in detail how they were computed. We demonstrate BioDeducta by showing how it can approximately replicate a previously published analysis of genes involved in the adaptation of cyanobacteria to different light niches. Conclusions/Significance.

In the CAT Tournament, specialists facilitate transactions between buyers and sellers with the intention of maximizing profit from commission and other fees. Each specialist must find a well-balanced strategy that allows it to entice buyers and sellers to trade in its market while also retaining the buyers and sellers that are currently subscribed to it. Classification techniques can be used to determine the distribution of bidding strategies used by all traders subscribed to a particular specialist. Our experiments showed that Hidden Markov Model classification yielded the best results. The distribution of strategies, along with other competition-related factors, can be used to determine the optimal action in any given game state. Experimental data shows that the GD and ZIP bidding strategies are more volatile than the RE and ZIC strategies, although no traders ever readily switch specialists. An MDP framework for determining optimal actions given an accurate distribution of bidding strategies is proposed as a motivator for future work. 1

This paper discusses how Artificial Intelligence (AI) could be used for decision support in modern intensive care units (ICUs), namely using knowledge-based techniques. First, the specific needs for decision support in ICU will be analyzed, which results in the most urgent need with regard to the different tasks of monitoring and therapy planning. Second, a definition for AI will be presented. Third, methods to solve the two essential parts of monitoring and therapy planning, namely data validation and abstraction in real-world environments, will be exemplified. Finally, basic requirements and limitations for knowledgebased decision support will be summarized.

planning Abstract. Modern animation packages provide partial automation of action between key frames. However the creation of scenes involving many interacting characters still requires most of the work to be hand-done by animators and any automatic behavior in the animation sequence tends to be hard-wired and lacking autonomy. This paper describes our “FreeWill” prototype which addresses these limitations by proposing and implementing an extendable cognitive architecture designed to accommodate goals, actions and knowledge, thus endowing animated characters with some degree of autonomous intelligent behavior. 1

EOPLE with spinal cord injury (SCI) are often unable to move their limbs, though most of their nerves and muscles may be intact. Functional Electrical Stimulation (FES) can

Functional Electrical Stimulation (FES) • People with spinal cord injury (SPI) are often unable to move their limbs, though most of their nerves and muscles may be intact.

Abstract — The world globalization is widely used, and there are several definitions that may fit this one word. However the reality remains that globalization has impacted and is impacting each individual on this planet. It is defined to be greater movement of people, goods, capital and ideas due to increased economic integration, which in turn is propelled, by increased trade and investment. It is like moving towards living in a borderless world. With the reality of globalization, the travel industry has benefited significantly. It could be said that globalization is benefiting from the flight industry. Regardless of the way one looks at it, more persons are traveling each day and are exploring several places that were distant places on a map. Equally, technology has been growing at an increasingly rapid pace and is being utilized by several persons all over the world. With the combination of globalization and the increase in technology and the frequency in travel there is a need to provide an intelligent application that is capable to meeting the needs of travelers that utilize mobile phones all over. It is a solution that fits in perfectly to a user’s busy lifestyle, offers ease of use and enough intelligence that makes a user’s experience worthwhile. Having recognized this need, the Agent based Mobile Airline Search and Booking System is been developed that is built to work on the Android to perform Airline Search and booking using Biometric. The system also possess agent learning capability to perform the search of Airlines based on some previous search pattern.The development been carried out using JADE-LEAP Agent development kit on Android. Keywords- Agents; Biometric; JADE-LEAP; Android. I.