NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

This My thesis work combines AI, programming language design, incompleteness of perception and dynamism in the environment and software engineering. I am integrating reinforcement creates a strong need for adaptivity. Programming this learning (RL) into a programming language so adaptivity by hand in a language that does not provide builtin that the language achieves three primary goals: accessibility, support for adaptivity is very cumbersome. As I demonstrated adaptivity, and modularity. If I am successful, my or designer specifies the structure of certain parts work will enable a discipline of modular large-scale agent of a program while leaving other portions unspecified, such software engineering while making advanced agent modeling that a learning system can learn how to perform them.

We present a new robot platform, the Finch, that was designed to align with the learning goals and concepts taught in introductory computer science courses. The Finch was developed in the context of the CSbots program, the goal of which is to improve retention and learning in computer science courses through the use of robots and other physically embodied hardware. This paper concentrates on design constraints that were determined in earlier CSbots studies and how those constraints were instantiated by the Finch. We also present some preliminary results from pilot studies in which Finch robots were used in CS1 and CS2 classes.

In Australia, the Scientists-in-Schools program partners professional scientists with teachers from K-12 schools to improve early engagement and educational outcomes in the sciences and mathematics.  An overview of the developing syllabus of a K-6 course resulting from the pairing of a senior AI researcher with teachers from a K-6 (primary) school is presented. Now entering its third year, the course introduces the basic concepts, vocabulary and history of science generally and AI specifically in a manner that emphasises student engagement and provides a challenging but age appropriate syllabus. Reflecting on the course at this time provides an action research basis for ongoing maturation of the syllabus, and the paper is presented in that light.

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.

In this paper, we describe the outline for a course-long information retrieval (IR) project. The project guides the students in constructing a working IR system from the ground up. The first half of the project is structured and closely follows common foundational IR concepts. During this portion of the project, a bare-bones IR system is constructed. For the last half of the project, students (in groups) implement research-driven extensions to the basic system with the additional constraint that their project must integrate with the base system. By the end, the students have worked on a large software project (~40 classes with thousands of lines of code) in a group setting as well as been introduced to the research process. This project plan has been successfully used in an undergraduate course; resources including starter code, solutions, and an example IR system with project write-ups are available.

Mixed reality is an important classroom tool for managing complexity from both the students' and instructor's standpoints. It can be used to provide important scaffolds when introducing robotics, by allowing elements of perception and control to be abstracted, and these abstractions removed as a course progresses (or left in place to introduce robotics to younger groups of students). In prior work, we have illustrated the potential of this approach both in providing scaffolding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this paper, we explore integrating extensions and improvements to the mixed reality components themselves as part of a course in applied artificial intelligence and robotics. We present a set of assignments that in addition to exploring robotics concepts, actively integrate creating or improving mixed reality components. We find that this approach better leverages the advantages brought about by mixed reality in terms of student motivation, and also provides some very useful software engineering experience to the students.

The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

A research project aimed at the development of an automated theorem proving system was started in Kiev (Ukraine) in early 1960s. The mastermind of the project, Academician V.Glushkov, baptized it "Evidence Algorithm", EA. The work on the project lasted, off and on, more than 40 years. In the framework of the project, the Russian and English versions of the System for Automated Deduction, SAD, were constructed. They may be already seen as powerful theorem-proving assistants.