Task ontology based authoring system for training systems

Recently much attention has been paid to the notion of "ontology" in the expectation that it can serve as the new, strong foundation of knowledge engineering. In the conventional approach to theory of knowledge, to give the operational semantics of knowledge representation has been regarded as of major importance and the analysis of contents of knowledge has been considered to be subordinate to it. To solidify the foundation of knowledge engineering, however, the many researchers , especially in the field of knowledge sharing and reuse, has strongly felt necessity of the change of such a way of thinking. The key to the problem is to understand the essential interaction between "form" and "contents" on equal importance. This implies that deep understanding of "content" will give us new insight into design of knowledge representation. The notion of "ontology" can be key to this issue.

The ultimate goal of research on ontology is to give the full picture of theory of knowledge. To make improvements in the study of this difficult issue, of course, it is important to accumulate huge amount of "contents", and develop sophisticated ontology representation language as fundamental "form" of knowledge.

The same thing applies to the field of intelligent educational systems (IES). Building an IES requires a lot of work. At the present situation, however, it is always built from scratch. Little functional components are reusable and we cannot compare or assess the existing systems. Only existing contribution to the solution of the problem can be found in study of the authoring tools for educational systems. However, it is considered questionable whether substantial benefit for the authors engaged in the complex task may be expected or not, since most of existing authoring tools do not satisfy the requirements for the authoring tools as shown below.

• To provide human-friendly primitives in terms of which authors can easily describe define their own skeleton of IES.
• To give appropriate guidance to authors based on the established principle of the educational task by checking the rationality of the skeleton of IES.
• To show the dynamic behavior of the IES in conceptual level by which the authors can examine its validity.

We think the key to the solution of the problem is intelligent support based on "task ontology" which serves as a theory of vocabulary/concepts used as building blocks for knowledge-based systems. The issues here also include how to represent what we know about the fundamental characteristics of an IES as "task ontology" and how to integrate it into intelligent authoring tools. Our solution is integration of an ontology construction environment CLEPE as a part of the authoring tool we have developed. CLEPE provides us with all the functions needed to satisfy the requirements shown above.

The most important role of CLEPE is to lay the theoretical foundation for IES development process. It maintains continuity from author's conceptual understanding of educational task to the computational semantics of IESs. It provides human friendly vocabulary for authors to describe the educational task. For the authoring tools, on the other hand, it specifies the computational semantics of vocabulary and also provides a set of components represented in terms of both conceptual primitives and object-oriented code fragments.

The goals of our research on task ontology are to exemplify the benefits of task ontology through the development of an ontology based authoring tool for Computer Based Training (CBT) systems. In this paper, we will discuss the basic issues on the concept of task ontology and then describe the design principle of an ontology-based authoring tool for Computer Based Training (CBT) systems.

Major publications

1. A Multiple View Authoring Tool for Modeling Training Materials, AI Technical Report 99-05, I.S.I.R., Osaka University, July, 1999
2. Ontology-aware systems in AI-ED research, AI Technical Report 99-04, I.S.I.R., Osaka University, April, 1999.
3. Ontological Engineering of Instruction: A Perspective, Proc. of AIED'99, (to appear)
4. An Ontology-Aware Authoring Tool - Functional structure and guidance generation -, Proc. of AIED'99, (to appear)
5. An Ontology-based Intelligent Authoring Tool, Proceedings of ICCE'98, pp.41-49, Beijing, China, 1998(Also in Workshop Notes of Model based reasoning for intelligent education environments, ECAI98, Brighton, UK, 1998)..
6. Task Ontology Makes It Easier To Use Authoring Tools, Proc. of IJCAI-97, pp.342-347,1997
7. Roles of Shared Ontology in AI-ED Research -- Intelligence, Conceptualization, Standardization, and Reusability --]Proc. of AIED-97, pp.537-544,1997
8. Task Ontology Design for Intelligent Educational/Training Systems. Workshop "Architectures and Methods for Designing Cost-Effective and Reusable ITSs, ", ITS'96, 1996
9. Knowledge Engineering of Educational Systems for Authoring System Design. Prof. of Euro AIED, pp.329-335, 1996.

SmartTrainer is a Computer Based Training System including a set of simulators in the area of Electric Power System. The target task of SmartTrainer is mainly to recover the accidents of substations in the electric power system. When an accident happens, the electric power transmission will be interrupted, and the operators should recover it as quickly as possible. The operators should find the spot of the accident, continue to supply the electric power to some special places such as hospital, police station at once by borrowing some power from the other substations, find the causes of the accident and recover it within the limited time.

The goal of the training oriented by SmartTrainer is to improve capability of not only skill-based or rule-based reasoning but also knowledge-based reasoning. The set of the scenarios incorporated into SmartTrainer has been designed by the experienced trainers.In order to let the trainee master the principled knowledge, SmartTrainer let them do practice first and then teach them the first principle behind it adaptively to their mistakes, and finally, check their learning result by practice(training) again. With the cycle of practice->knowledge->practice, teaching process is going forward. This is a form of "learning by doing".

Here we want to emphasize that the training we give to the trainee has the time-limitation just like in real accidents. Multi-media technique has been widely used in SmartTrainer to attain high fidelity, including the sound processor to create mock buzzer when an accident happens, the movie display to show the accident scene when the repairing man needs, the picture processor to create the static graphics of the various equipment, and so on.

SmartTrainer is composed of five parts, those are human interface, authoring, training model based on the training ontology, teaching materials model based on the teaching materials ontology and simulator. Here we will discuss the designing of authoring environment based on task ontology in SmartTrainer mainly

Major publications

1. Role Explication of Simulation in Intelligent Training Systems by Training Task Ontology, Proc. of AI-ED-97 Workshop
2. Ontological Issues on Computer-based Training, Proc. of PRICAI-95 Workshop