An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.

Personal use of this material is permitted. Abstract --The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation. Safety assessment of automated driving functions is an emerging topic in the automotive industry. Several research and development projects show prototypes of automated vehicles in well-defined showcases. When it comes to series production, the ISO 26262 standard defines a state-of-the-art development process to ensure functional safety. Automated vehicles will have to fulfill a safe driving task in a high number of operating scenarios. To comply with the hazard analysis and risk assessment demanded by the ISO 26262 standard, hazardous events "shall be determined systematically by using adequate techniques" [1, Part 3].

Summary: IBM's Watson now to do your taxes at H&R Block? This is a good opportunity to explore the differences between Question Answering Machines (Watson) and Expert Systems. If you were paying attention during the Super Bowl you saw something unprecedented, an advertisement aimed at data scientists. It was the H&R Block announcement that it was rolling out IBM's Watson to all 80,000 of its tax preparers. So far we've seen Watson deployed primarily on more complex and obscure data like chemical reactions, cancer diagnoses, and environmental engineering.

This new conference series promotes multidisciplinary research on tools and methodologies for efficiently capturing knowledge from a variety of sources and creating representations that can be (or eventually can be) useful for reasoning. The conference attracted researchers from diverse areas of AI, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem solving and reasoning, planning, agents, text extraction, and machine learning. Knowledge acquisition has been a challenging area of research in AI, with its roots in early work to develop expert systems. Driven by the modern internet culture and knowledge-based industries, the study of knowledge capture has a renewed importance. Although there has been considerable work over the years in the area, activities have been distributed across several distinct research communities.

Thsi article is a slightly modified version of an invited address that was given at the Eighth IEEE Conference on Artificial Intelligence for Applications in Monterey, California, on 2 March 1992. It describes the lessons learned in developing and implementing the Artificial Intelligence Research and Development Program at the National Aeronautics and Space Administration (NASA). In so doing, the article provides a historical perspective of the program in terms of the stages it went through as it matured. These stages are similar to the "ages of artificial intelligence" that Pat Winston described a year before the NASA program was initiated. The final section of the article attempts to generalize some of the lessons learned during the first seven years of the NASA AI program into AI program management heuristics.

Artificial Intelligence for Microcomputers If you would like to develop an expert system or knowledgebased system on a microcomputer, you might want to read Artijcial Intelligence for Microcomputers by Mickey Williamson, This nontechnical book is easy to understand, written for the unsophisticated microcomputer user. The first chapters provide a brief history of artificial intelligence (AI) and an introduction to natural language query systems. They explain what knowledge-based systems and expert systems are and how they work. Discussions are also provided of the two major AI programming languages, Lisp and Prolog, including their strengths and weaknesses. The remainder of the book is devoted to a review of some of the existing AI software products for microcomputers, such as natural language query systems, decision support systems, expert system development shells, and AI programming languages.

Editor: On "Learning Language" I was dismayed by the inclusion of William Katke's article ("Learning Language Using A Pattern Recognition Approach," Spring 1985). Usually you do an excellent job of representing "the current state of the art in Artificial Intelligence" (to quote your Editorial Policy), but I consider this article an exception. First of all, although the article claims to be on "Learning Language," what it presents is at best a knowledge-free approach to learning syntax. I saw no evidence that the induced syntax is useful for anything, and good reasons to believe that it is not, such as the unmnemonic category names and the intrinsic limitations of finite state grammars. Second, this kind of stuff has been done before, and it didn't work too well then either; for a useful overview of the field and pointers into the literature, see the article on "Grammatical Inference" in Volume 3 of The Handbook of The plete specifications and the verification of proposed impleideas and issues presented were firmly focused on a conven-mentations, we should concentrate more on incremental tional view of the design process-a view I can caricaturize development of specifications as a result of assessment of as the SPIV methodology: performance.

The Electrical Systems Division at the NASA Kennedy Space Center has developed and deployed an agent-based tool to monitor the space shuttle's ground processing telemetry stream. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation.

Organizations are adaptive systems that continually attempt to push the limits of their own effectiveness to approach perfection. This approach is true of the "mom and pop" store that is threatened by the growth of shopping malls. It is true of the gigantic corporation that is threatened by public regulation and private competition. It is particularly true of organizations that are confronted with complex tasks, the vagaries of uncertainty, and the high and visible costs of irreversible error. The cause of organization ineffectiveness or, indeed, failure is often perceived to be human frailty (Perrow 1984).

The annual Workshop on the Validation and Verification of Knowledge-Based Systems is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). The 1994 workshop was significant in that there was a definitive move in the philosophical position of the workshop from a testing-and toolbased approach to KBS evaluation to that of a formal specification-based approach. This workshop included 12 full papers and 5 short papers and was attended by 35 researchers from government, industry, and academia. The workshop is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). It has influenced the evolution of the discipline from its origins in 1988; at this time, researchers were asking the questions, How can we evaluate the correctness of KBS? How is this process different from conventional system evolution?