This paper presents a methodology to acquire new knowledge in TutorJ using external information sources. TutorJ is an ITS whose architecture is inspired to the HIPM cognitive model, while meta-cognition principles have been used to design the knowledge acquisition process. The system behavior is intended to increase its own knowledge as a consequence of the interaction with users. The implemented methodology uses external links and services to capture new knowledge from contents related to discussion topics and transforms these contents into structured knowledge that is stored inside an ontology. The purpose of the proposed methodology is to lower the effort of system scaffolding creation and to increase the level of interaction with users. The focus is on self-regulated learners while meta-cognitive strategies have to bee defined to adapt and to increase the effectiveness of tutoring actions.

This paper deals with computational approaches to storytelling, or the production of stories by computers, with a particular attention on the way human creativity is modelled or emulated, also in computational terms. Features relevant to creativity and to stories are analysed, and existing systems are reviewed under the light of that analysis.The extent to which they implement the key features proposed in recent models of computational creativity is discussed. Limitations, avenues of future research and expected trends are outlined.

When configuring customizable software, it is useful to provide interactive tool-support that ensures that the configuration does not breach given constraints. But, when is a configuration complete and how can the tool help the user to complete it? We formalize this problem and relate it to concepts from non-monotonic reasoning well researched in Artificial Intelligence. The results are interesting for both practitioners and theoreticians. Practitioners will find a technique facilitating an interactive configuration process and experiments supporting feasibility of the approach. Theoreticians will find links between well-known formal concepts and a concrete practical application.

Motivated by Zadeh's paradigm of computing with words rather than numbers, several formal models of computing with words have recently been proposed. These models are based on automata and thus are not well-suited for concurrent computing. In this paper, we incorporate the well-known model of concurrent computing, Petri nets, together with fuzzy set theory and thereby establish a concurrency model of computing with words--fuzzy Petri nets for computing with words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of transitions are some special words modeled by fuzzy sets. By employing the methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which makes it possible for computing with more words. The language expressiveness of the two formal models of computing with words, fuzzy automata for computing with words and FPNCWs, is compared as well. A few small examples are provided to illustrate the theoretical development.

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans.  Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits the assumption that actions change the state of objects, and require objects to be in a certain state before they can be executed.  The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences.  In this paper we describe the implemented LOCM algorithm, and analyse its performance by its application to the induction of domain models for several domains. To evaluate the algorithm, we used random action sequences from existing models of domains, as well as solutions to past IPC problems.

Realistic rendering is the process of generating a 2D image from an abstract description of a 3D scene, aiming at achieving the quality of a photo. The quality of the generated image depends on the accuracy with which the employed render method simulates the behaviour of the light particles through the scene. According to the current practice, it is up to the user to choose optimal settings of input parameters for these methods in terms of time-efficiency, as well as image quality. This is an iterative trial and error process, even for expert users. This paper describes a novel approach based on techniques from the field of data mining and genetic computing to assist the user in the selection of render parameters. Experimental results are presented which show the benefits of this approach.

The goal of the Pedagogical Discourse project is to develop instructional tools that will help students and instructors use discussion boards more effectively, with an emphasis on automatically assessing discussion activities and building tools for promoting student discussion participation and learning. In this paper, we present a two related participation and learning scaffolding tools that exploit natural language processing and information retrieval techniques. The PedaBot tool is designed to aid student knowledge acquisition and promote reflection about course topics by connecting related discussions from a knowledge base of past discussions to the current discussion thread. The MentorMatch tool aims at promoting student participation using student mentors, i.e., course peers with a relatively good understanding of a particular topic. The system identifies students who often provide answers on a given topic and encourages classmates to invite mentors to participate in related discussions. Both tools have been integrated into a live discussion board that is used by an undergraduate computer science course. This paper describes our approaches to applying information retrieval and natural language processing techniques in the development of the tools and presents initial results from instrumentation and survey.

CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.

This paper explores the application of knowledge-based Word Sense Disambiguation systems to specific domains, based on our state-of-the-art graph-based WSD system that uses the information in WordNet. Evaluation was performed over a publicly available domain-specific dataset of 41 words related to Sports and Finance, comprising examples drawn from three corpora: one balanced corpus (BNC), and two domain-specific corpora (news related to Sports and Finance). The results show that in all three corpora our knowledge-based WSD algorithm improves over previous results, and also over two state-of-the-art supervised WSD systems trained on SemCor, the largest publicly available annotated corpus. We also show that using related words as context, instead of the actual occurrence contexts, yields better results on the domain datasets, but not on the general one.  Interestingly, the results are higher for domain-specific corpus than for the general corpus, raising prospects for improving current WSD systems when applied to specific domains.

We present a method to decompose a declarative knowledge base, given by a logic program under Answer Set Semantics with access to external sources. It overcomes the ineffectiveness of current methods due to a lack of structural information about these sources, viewed as black boxes, by exploiting independency information in accesses to them.  To this end, we develop a generic notion of domain independence that allows to restrict the evaluation domain and, as a consequence, to prune unnecessary dependency assumptions between atoms. This leads to increased decomposability, which we demonstrate by an evaluation method for HEX-programs based on program rewriting. Experiments show that this may yield large performance gains. While developed for a particular formalism, the notions and ideas of this  paper might be adapted to related formalisms as well.