Another way to speed up a RL algorithm is by using Transfer Learning, a paradigm of machine learning that In this paper we propose to combine three AI techniques reuses knowledge accumulated in a previous task to speed up to speed up a Reinforcement Learning algorithm the learning of a novel, but related, target task [Taylor and in a Transfer Learning problem: Casebased Stone, 2009]. Reasoning, Heuristically Accelerated Reinforcement This paper investigates the use of the Case-Based Heuristically Learning and Neural Networks. To do Accelerated Reinforcement Learning (CB-HARL) algorithm so, we propose a new algorithm, called L3, which [Bianchi et al., 2009] as a means to transfer learning works in 3 stages: in the first stage, it uses Reinforcement acquired by one agent during its training in one problem to Learning to learn how to perform one another agent that has to learn how to solve a similar, but task, and stores the optimal policy for this problem more complex, problem. To do so, we propose a new algorithm, as a case-base; in the second stage, it uses a Neural called L3, which works in 3 stages: in the first stage, Network to map actions from one domain to actions it uses the Q-learning algorithm [Watkins, 1989] to learn how in the other domain and; in the third stage, it uses to perform one task, and stores the optimal policy for this the case-base learned in the first stage as heuristics problem as a case-base; in the second stage, it uses a Neural to speed up the learning performance in a related, Network to map actions from one domain to actions in but different, task. The RL algorithm used the other domain and; in the third stage, it uses the case-base in the first phase is the Q-learning and in the third learned in the first stage as heuristics in the CB-HARL algorithm, phase is the recently proposed Case-based Heuristically speeding up the learning process.

Willow is a free-text Adaptive Computer Assisted Assessment system, which supports natural language processing and user modeling. In this paper we discuss the benefits coming from extending Willow with recommendations. The approach combines human computer interaction methods to elicit the recommendations with data mining techniques to adjust their definition. Following a scenario-based approach, 12 recommendations were designed and delivered in a large scale evaluation with 377 learners. A statistically significant positive impact was found on indicators dealing with the engagement in the course, the learning effectiveness and efficiency, as well as the knowledge acquisition. We present the overall system functionality, the interaction among the different subsystems involved and some evaluation findings.

This article introduces a reflexion about behavioural specification for interactive and participative agent-based simulation in virtual reality. Within this context, it is neces sary to reach a high level of expressivness in order to enforce interactions between the designer and the behavioural model during the in-line prototyping. This requires to consider the need of semantic very early in the design process. The Intentional agent model is here exposed as a possible answer. It relies on a mixed imperative and declarative approach which focuses on the link between decision and action. The design of a tool able to simulate virtual environment implying agents based on this model is discuss

See Leuski et al. (2006) and to the same question -- for example, "What Leuski and Traum (2008) for more details. is your name?" -- depending on who the interactor The final parameter is the classification threshold is looking at. NPCEditor's user interface allows the on the KL-divergence value: only answers that designer to define arbitrary annotation classes or score above the threshold value are returned from categories and specify which of these annotation the classifier. The threshold is determined by tuning categories should be used in classification.

Using the Hilbert-Bernays account as a spring-board, we first define four ways in which two objects can be discerned from one another, using the non-logical vocabulary of the language concerned. (These definitions are based on definitions made by Quine and Saunders.) Because of our use of the Hilbert-Bernays account, these definitions are in terms of the syntax of the language. But we also relate our definitions to the idea of permutations on the domain of quantification, and their being symmetries. These relations turn out to be subtle---some natural conjectures about them are false. We will see in particular that the idea of symmetry meshes with a species of indiscernibility that we will call `absolute indiscernibility'. We then report all the logical implications between our four kinds of discernibility. We use these four kinds as a resource for stating four metaphysical theses about identity. Three of these theses articulate two traditional philosophical themes: viz. the principle of the identity of indiscernibles (which will come in two versions), and haecceitism. The fourth is recent. Its most notable feature is that it makes diversity (i.e. non-identity) weaker than what we will call individuality (being an individual): two objects can be distinct but not individuals. For this reason, it has been advocated both for quantum particles and for spacetime points. Finally, we locate this fourth metaphysical thesis in a broader position, which we call structuralism. We conclude with a discussion of the semantics suitable for a structuralist, with particular reference to physical theories as well as elementary model theory.

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.

Tekkotsu is an open source application development framework for intelligent mobile robots. Originally designed for undergraduate computer science majors, recent refinements to the framework have led us to explore its use with high school students. We developed a pilot course curriculum to introduce high level robotics to students with little or no programming experience in a way that provides improved feedback and error detection on multiple levels. The use of visualization tools and pair programming techniques scaffolds the learning process and provides a systematic way to introduce robotics as a fun and worthwhile endeavor to novices, and helps instructors efficiently address students’ concerns in a real-time manner.

In this study, we analyze two corpora of art critiques: one on the subject of photography and the other on the subject of modern art. We use two computational tools, the Gramulator and GPAT to analyze both sets of texts. The Gramulator was used to show the indicative linguistic features that make photography criticism a distinct genre from modern art criticism. Results suggest that lexical features, structural formats, and genre consistency differed significantly between the two corpora. The findings provide information for teachers, students, publishers, and curriculum developers for creating more effective writing and teaching materials. This includes material for English for Specific Purposes (ESP) in the form of textbooks, workbooks and other external learning material.

In this paper we present Myro-C++, developed at the University of Tennessee. Myro-C++ is a C++ port ofthe Python Myro library that was written by the Institute for Personal Robots in Education (IPRE) at Georgia Tech and Bryn Mawr College. Myro-C++ is publicly available, open source software, released under the GPLv3 open source license. At the time of writing, the library has been used six semesters for the CS1 courseat the University of Tennessee, Knoxville. The library contains functions for control of the robot and access to sensor information, and provides the ability to display the live camera image from the robot into a video window. This library is used as a teaching tool in our CS1 course where students learn basic programming fundamentals using multiple artificial intelligence based labs. In addition to the software, the IPRE book, Learning Computing with Robots, has been edited to use C++ examples and explanations, and is freely available. We also present example programs that we use as laboratory assignments in our Introduction to Computer Science course, which are also freely available.

We describe a four-week series of assignments in an undergraduate AI course at a liberal arts college developing a supervised learning solution to the problem of classifying Twitter accounts as either a person account or a non-person account (e.g. organization or spambot). This problem employs real data in an ongoing research project by the first author, yet is accessible to students with limited programming expertise.The students were able to experience a complete cycle of creating a machine learning solution: exploring raw data,creating a training set, engineering features, comparing different classifiers, evaluating the results, and performing erroranalysis. We received positive feedback from the students and intend to refine the assignment and make it available (together with the created training data) for use by the research community.