Causality plays an important role in our comprehension of the world. It amounts to determine what truly causes what and what it matters. Interventions allow the identification of elements in a sequence of events that are related in a causal way. In this paper, we introduce belief causation and we proposea method for handling interventions in graphical model under an uncertain environment where the uncertainty is represented by belief masses, so-called belief causal networks. More specifically, we propose a generalization of the “DO” operator and explain the needed changes on the structure of the graph to model a belief causal network on which interventions are proceeded.

Many problems in AI (in reasoning, planning, learning, perception and robotics) require an agent to operate with incomplete or uncertain information. The objective of this track is to present and discuss a broad and diverse range of current work on uncertain reasoning, including theoretical and applied research based on different paradigms. We hope that the variety and richness of this track will help to promote cross fertilization among the different approaches for uncertain reasoning, and in this way foster the development of new ideas and paradigms. The Special Track on Uncertain Reasoning is the oldest track in FLAIRS conferences, running annually since 1996. This meeting will mark the 16th in the series.

Teaching an upper-level undergraduate robotics course at Historically Black Colleges and Universities (HBCUs) is challenging. The lack of suitable teaching materials is one of the biggest challenges, although there are many great masterpieces in developing robotics course materials, which are, however, generally developed for teaching students at elite Research I universities. This paper presents ideas and details in adopting and revising these course materials and creating upper-level undergraduate robotics course materials that are suitable for underrepresented students.

The Snackbot, is a robot designed in collaboration between the Robotics Institute, and the Human Computer Interaction Institute of Carnegie Mellon University. The Snackbot was created to traverse the halls of Carnegie Mellon University, and deliver food items ordered by occupants of the offices. The goal of this development project for the Snackbot, was to refine the audio/video synchronization, and to also create a simple way to log, and stream that data over a network. Such a task requires that one not only carefully consider different pieces of software to use, but also that they can apply it across the necessary platform. For the Snackbot, the sight, and sound are important qualities, especially when testing out in the field using an operator. That ability is crucial when preparing an interactive robot to autonomously carry out its task efficiently.

Tekkotsu is a free, open source software framework for high-level robot programming. We describe enhancements to Tekkotsu's navigation component, the Pilot, to incorporate a particle filter for localization and an RRT-based path planner for obstacle avoidance. This allows us to largely automate the robot's navigation behavior using a combination of odometry and landmark-based localization. Beginning robot programmers need only indicate a destination in Tekkotsu's world map and the Pilot will take the robot there. The software has been tested both in simulation and on Calliope, a new educational robot developed in the Tekkotsu lab in collaboration with RoPro Design, Inc..

Multi-robot task allocation is an important problem for heterogeneous mobile robots. Simultaneous allocations with which multiple tasks are being allocated concurrently tend to lead to more efficient allocations than online or single task allocations. However, the simultaneous allocation also increases the complexity in the winner determination process, especially when robots are required to collaborate in order to accomplish certain tasks. This paper presents a winner determination algorithm for the simultaneous allocation of multi-robot tasks. The complete approach layers alow-level coalition formation algorithm for solving one multi-robot task with a high-level simultaneous task allocation approach. We implement a tree-based winner determination algorithm with an iterative deepening A* (IDA*) search and show that the algorithm is able to generate the optimal task-coalition mapping in the initial round and the IDA* performs efficiently based on time and space complexities.

The mission of the ARTSI (Advancing Robotics Technology for Societal Impact) Alliance, a consortium of 19 Historically Black Colleges and Universities (HBCUs) and 9 major research universities (R1s), is to enlarge the nation’s engineering and science talent pool by increasing the number of students from underrepresented groups who pursue advanced training in computer science. ARTSI is one of several alliances funded by the National Science Foundation’s Broadening Participation in Computing Program. ARTSI focuses specifically on institutions serving African Americans and uses robotics education to attract and engage students. In this paper we describe the activities comprising ARTSI, our vision of a robotics curriculum for CS undergraduates, and ways to integrate robotics modules into existing CS courses.

Robotics is a multidisciplinary area of study across computer science, electrical engineering, and mechanical engineering. Robotics covers the study, design, manufacture, and use of robots in various applications. Robotics, computer vision, activity recognition, path planning, and the many other disciplines where computers interface to physical environments have proven to be a major source of inspiration and crucial new insights into artificial intelligence. Human-robot interaction has become a major concern as many robots have been used in real-world applications. This track focuses on all aspects of robotics, including related areas and applications, including robotics education.

In this paper we propose an ontological framework for tools facilitating creative writing and story reading. It is based on an ontology implemented as a topic map and employs linguistic analysis methods for discovering conceptual entities in the text.

Various strategies and techniques have been proposed for the generation of questions/answers tests in Intelligent Tutoring Systems by using OWL (Web Ontology Language) ontolo- gies. Currently there have been no known methods to utilize SWRL rules for this task. This paper presents a system and a set of strategies that can be used in order to automatically generate multiple choice questions from SWRL rules. The aim of the proposed framework is to support further research in the area and to be a testbed for the development of more advanced assessment techniques.