Goal-Based Autonomous Social Agents: Supporting Adaptation and Teaching in a Distributed Environment

This paper proposes a theoretical framework that allows goal -based agents attached to networked applications and learning environments to support users’ work and learning. Users, learners, applications and learning environments are represented by autonomous goal -based social agents who communicate, cooperate, and compete in a multi-system and multi-user distributed environment. This allows for a uniform approach to support the user while working, by adaptation to the user's goals, preferences, level of experience and available resources; as well as teaching the user using various teaching paradigms (consrtuctivist or instructivist). In addition it allows one to take into account the user’s /learner’s motivation and affect; as well as enabling a coherent discussion of teaching strategies. 1 Trends in the Development of Adaptive and Teaching Systems Two major trends can be observed in the development of learning environments, which follow from the rapid development of networking and communication technologies: • An integration of working and learning environments. • No virtual difference between humans and application agents Nowadays nearly all commercial applications (most prominently CorelDraw, Toolbook etc.) are equipped with training programs, which provide an introduction into the main features and basic working techniques, as well as with on-line help, which is in some cases context-sensitive and even adaptive (MS-Office 97). This means that the user is working and learning at the same time, and can switch from "working" mode to "learning" mode at will. It is easy to switch from some type of teaching or demonstration, which will help the user learn about something specifically needed at the moment, and then switch back to the "working" mode to try and use the newly acquired knowledge in practice. * Published in Proceedings of ITS'98, San Antonio, Texas, pp.564 -573 From the other side, learning environments specifically designed for educational purpose in some subject are often inspired by constructivist and Vygodskian theories of learning, which focus on context-anchored learning and instruction, that takes place in the context of solving a realistic problem. There is a tendency in learning environment design philosophies towards integrating work and learning; work being the source of problems and motivation for learning. In general, one can observe a convergence between working environments and learning environments. For example, instead of adapting to sub -optimal learner behavior, the system may decide to teach the learner (instruct, explain, provide help, etc.) how to do things correctly, i.e. to make the user adapt to the system by learning. In reality every adaptation is bi-directional. Every participant in an interaction process adapts to the other participant(s). The system learns about the user and adapts to him/her; the user learns about the system and adapts accordingly. An adaptive system should support the user’s learning about the system [8]. It has to be able to decide whether to adapt to the user or to teach something instead (i.e. to make the user adapt to the system). It must decide whether to be reactive or proactive. In this way the system will be an active participant in the interaction, an autonomous agent, which can decide in the course of interaction and not just follow embedded decisions made at design time (normative decisions). An attempt to build such a system which can take decisions about whether to teach or to coach the student depending on the context of interaction and state of student model has been designed and implemented using reactive planning techniques [8]. However, we feel that such a pedagogically "competent" system has to be able to negotiate its decisions with the learner and not just impose them, since no mater what expertise is underlying these decisions, there is always uncertainty about the correctness of this knowledge and about the student model. Therefore we decided to model the pedagogical component in an intelligent learning environment as an autonomous agent that pursues certain teaching goals. These goals can be cognitive (subjectand problem-specific), motivational, and affective (learnerand subject-specific). We call these agents "Application agents", since they are associated with an application which can be in a special case, a learning environment. Since the user / learner is also an autonomous agent pursuing his / her own goals, the decision of which and whose goals will be pursued (the pedagogical agent's or the learner') is made interactively, in a process of negotiation and persuasion. In pursuing its goals, an application agent uses explicitly its relationship with the user/ learner. It can modify the parameters of the relationship, so that it can adopt user goals or learner goals and provide resources for them (achieving in this way explorative learning), infer and adapt to the learner's goals (to provide adaptive help or coaching) or try to make the learner achieve the teaching goals of the agent (to instruct the user / learner how to do something). The second major trend in the development of teaching systems is that there is no virtual difference between humans and application agents. It is no longer necessary that the teaching system is an almighty teacher knowing the answer to any question that may during the interaction /learning session. Networking provides a possibility to find somewhere else a system or a humanpartner who can help the learner with his/her problem and explain him/her themes that the system itself can not. This trend can be seen in the increasing work on collaborative learning systems, which are able to find appropriate partners for help or collaboration, to form teams and support goalbased group activities [1], [2]. For this purpose, it is imperative that teaching systems (and other computer applications providing adaptive help) be able to communicate information about their users (user models) and about their available resources and goals in order to find an appropriate partner. We can imagine application agents, attached to every application or learning environment, which have an explicit representation of the user's or application’s goals, plans, and resources. These agents communicate and negotiate among themselves for achieving their goals. This means that we need an appropriate communication language about goals and resources, which would allow these agents to share information. This communication has to be on a higher level than the level of knowledge communication (as in KQML or KIF), since it has a different purpose. While KQML and KIF have to define how the agents communicate their knowledge, this higher level of communication has the purpose to define who will be contacted, about what, when and how communication will take place (i.e. in which direction etc.). This level of communication has to be also transparent for humans, since some of the partners may be human-agents.