The increasingly prevalent view that recommendation is a conversation between user and system is driving a renewed interest in approaches to system design that involve the user in meaningful ways. In addition to this the proliferation of mobile devices and the near-ubiquity of sensing technologies means that there are now many opportunities to capture real-life experiences, in real-time, providing a new source of raw material for case-based reasoning. In this paper we consider the availability of real-world exercise information, in this cases corresponding to jogging routes, and meth- ods by which we can involve a user in recommending such routes. We describe the Exercise Builder, a proof-of-concept application that attempts to help visitors to a new city to plan their jogging routes by combining case retrieval, interactive adaptation, and multimedia explanation in a single online service.

While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems: "knowledge transfer". The issue we intend to elucidate can be expressed as follows: what knowledge present in a source case is transferred to a target problem in case-based inference? This paper presents a preliminary formal model of knowledge transfer and relates it to the classical notion of analogy.

Real-time Strategy (RTS) games are complex domains which are a significant challenge to both human and artificial intelligence (AI). For that reason, and although many AI approaches have been proposed for the RTS game AI problem, the AI of all commercial RTS games is scripted and offers a very static behavior subject to exploits. In this paper, we will focus on a case-based reasoning (CBR) approach to this problem, and concentrate on the process of case-acquisition. Specifically, we will describe 7 different techniques to automatically acquire plans by observing human demonstrations and compare their performance when using them in the Darmok 2 system in the context of an RTS game.

Conversational case-based reasoning systems use an interactive dialog to retrieve stored cases. Normally the ordering of questions in this dialog is chosen based only on their discriminativeness. However, because the user may not be able to answer all questions, even highly discriminative questions are not guaranteed to provide information. This paper presents a customization method CCBR systems can apply to adjust entropy-based discriminativeness considerations by predictions of user ability to answer questions. The method uses a naive Bayesian classifier to classify users into user groups based on the questions they answer, applies information from group profiles to predict which future questions they are likely to be able to answer, and selects the next questions to ask based on a combination of information gain and response likelihood. The method was evaluated for a mix of simulated user groups, each associated with particular probabilities for answering questions about each case indexing feature, in four sample domains. For simulated users with varying abilities to answer particular questions, results showed improvement in dialog length over a non-customized entropy-based approach in all test domains.

Robots are becoming increasingly common in home, industrial and medical environments. Their end users may know what they want the robots to do but lack the required technical skills to program them. We present a case-based reasoning approach for training a control module that controls a multi-purpose robotic platform. The control module learns by observing an expert performing a task and does not require any human intervention to program or modify the control module. To avoid requiring the control module to be modified when the robot it controls is repurposed, smart sensors and effectors register with the control module allowing it to dynamically modify the case structure it uses and how those cases are compared. This allows the hardware configuration to be modified, or completely changed, without having to change the control module. We present a case study demonstrating how a robot can be trained using learning by observation and later repurposed with new sensors and then retrained.

In this paper we describe ongoing undergraduate research projects that allow us to shift emphasis from teaching to a more active form of student participation. More specifically our projects are on automatic gesture recognition using the Kinect 3D sensor from Microsoft Research and machine learning systems. We have observed the following benefits for our undergraduate students: learning a topic area in AI relatively early; developing proficiency in laboratory practice, specifically, systematic data collection and programming on multiple platforms; learning to use appropriate methodology; applying knowledge to a real situation; learning to analyze data and transform it to various representations; appreciation of scientific experiments and learning what scientific research actually entails.

This paper presents a case study in combining Lego Mindstorms NXT with Matlab/Simulink to help students in an undergraduate Machine Learning course study genetic algorithm design and testing. The project uses the VU-LRT toolbox to enable students to access the hardware capabilities of the Mindstorms platform from within Matlab. The course's enrollment was comprised of students from several majors with a variety of programming backgrounds. The course is part of an interdisciplinary cognitive science concentration. We report on the VU-LRT toolbox, the considerations imposed by the diversity of the student population on the design of the laboratory module and student evaluations of the laboratory module.

The formal semantics of speech acts, even in the classical framework of illocutionary logic, requires considerations that go beyond individual speech activity and beyond the interpretation of individual sentences. We show how the formal semantics of speech acts can be extended to take into account the social effects and interactive aspects of illocutionary activity. To illustrate our approach, we focus on the semantics of assertions and descriptive discourse, contrasting the individual aspect of speaker's meaning and the epistemic effects of assertion making. The approach presented in this paper generalizes to all other types of illocutionary acts, adding specific content to the conversational record that registers the common ground of speakers and hearers as a dialogue unfolds.

The paper investigates a phenomenon of free word order through the analysis by reduction. It exploits its formal background and data types and studies the word order freedom by means of the minimal number of word order shifts (word order changes preserving syntactic correctness, individual word forms, their morphological characteristics and/or their surface dependency relations). The investigation focuses upon an interplay of two phenomena related to word order: (non-)projectivity of a sentence and number of word order shifts within the analysis by reduction. This interplay is exemplified on a sample of Czech sentences with clitics.

Several authors analyzed propositional attitudes ( wish , fear , regret , glad ) by integrating their epistemic and deontic components. This paper extends previous work done by the author and presents a logical calculus inspired by Possibility Theory, a non-compositional version of fuzzy logic.