We used a stimulus and response method in cognition to consider agents as situated in their specific (Binti Abdullah et al, 2010) to uncover correlation patterns context as it was realized that people are strongly affected of the physical artefact-communication during specific by, and possibly dependent on their environment contexts of communications. We found preliminary empirical (Susi & Ziemke, 2001). With this shift of focus, new interactive evidence that the physical artefacts influence the theories of cognition have emerged. These interactive communication process in a mutually constraining relationship theories such as situated cognition (Clancey, 1997), with the contexts. In which the context is made up and distributed cognition (Hutchins, 1999), are noted for of the teams' practice that includes how they collaborate, their emphasis on the relationship between cognition, and the physical setting, situations, and participation role.

In this paper we introduce our recommender Happy Movie, a Facebook application for movie recommendation to groups. This system exploits information about the social relationships and behaviour of the users to provide better recommendations. Our previous works have shown that social factors improve the recommendation results. However it required many questionnaires to be filled for obtaining the social information, so we have moved to a social network environment where this information is easily available.

If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge.

The combination of learning by demonstration and planning has proved an effective solution for real-time strategy games. Nevertheless, learning hierarchical plans from expert traces also has its limitations regarding the number of training traces required, and the absence of mechanisms for rapidly reacting to high priority goals. We propose to bring the game designer back into the loop, by allowing him to explicitly inject decision making knowledge, in the form of behavior trees, to complement the knowledge obtained from the traces. By providing a natural mechanism for designers to inject knowledge into the plan library, we intend to integrate the best of both worlds: learning from traces and hard-coded rules.

We present an efficient random decision tree algorithm for case-based reasoning systems. We combine this algorithm with a simple similarity measure based on domain knowledge to create a stronger hybrid algorithm. This combination is based on our general approach for combining lazy and eager learning methods. We evaluate the resulting algorithms on a case base of patient records in a palliative care domain. Our hybrid algorithm consistently produces a lower average error than the base algorithms.

In this paper we describe an evolutionary algorithm for assigning students to courses in a situation where each student specifies a set of courses in order of preference, each course has a limited enrollment, and the object is to maximize the overall student satisfaction by assigning each student to a course as high on his or her preference list as possible. Results of using the algorithm on historical data are compared to the success of a human in making the assignments. This work was done as part of a summer undergraduate research project while the second author was still a student. We also report preliminary results for using this problem as the basis for an assignment in a course in Artificial Intelligence.

Rook Jumping Maze design provides a number of good opportunities for experiential learning of AI concepts, including uninformed search, stochastic local search, machine learning, and objective/utility function design. In this paper we will define the maze and present a collection of exercises that allow exploration of several AI topics in the context of an engaging, fun, and unifying task.

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.

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 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.