Agents that Learn to Instruct

International Journal of Human-computer Studies / International Journal of Man-machine Studies, 2000

This paper presents a successful knowledge acquisition experiment in which subject matter experts that did not have any prior knowledge engineering experience succeeded to teach the Disciple-COA agent how to critique courses of action, a challenge problem addressed by the DARPA's High Performance Knowledge Bases program. We first present the COA challenge problem and the architecture of Disciple-COA, a learning agent shell from the Disciple family. Then we present the knowledge acquisition experiment, detailing both the expert-Disciple interactions, and the automatic knowledge base development processes that take place as a result of these interactions. The results of this experiment provide strong evidence that the Disciple approach is a viable solution to the knowledge acquisition bottleneck.

Foundations of intelligent tutoring systems, 1988

2018

The Cognitive Tutor Authoring Tools (CTAT) support creation of a novel type of tutors called example-tracing tutors. Unlike other types of ITSs (e.g., model-tracing tutors, constraint-based tutors), example-tracing tutors evaluate student behavior by flexibly comparing it against generalized examples of problem-solving behavior. Example-tracing tutors are capable of sophisticated tutoring behaviors; they provide step-by-step guidance on complex problems while recognizing multiple student strategies and (where needed) maintaining multiple interpretations of student behavior. They therefore go well beyond VanLehn's (2006) minimum criterion for ITS status, namely, that the system has an inner loop (i.e., provides within-problem guidance, not just end-of-problem feedback). Using CTAT, example-tracing tutors can be created without programming. An author creates a tutor interface through drag-and-drop techniques, and then demonstrates the problem-solving behaviors to be tutored. These...

Proceedings of the International Conference on Autonomous Agents, 2006

In this paper, we present an architecture for software agents that enables them to learn vocabulary through the observation of each other bodies and actions. Besides sensors, effectors, and action control, the architecture provides the equivalent of a body with visual appearance. The agent soft visual appearance is designed to be seen by other software agents, not by people. The paper describes the agent software body with visual appearance, the learning mechanism, the demonstration scenario and presents some results showing that the agent software body allows agents to learn vocabulary through observation and to ground the meaning of the symbols they learn. The paper emphasizes the important role of social interaction in learning processes.

1998

The paper presents some considerations about the impact of agents' and related technologies on educational software research and its application to designing and to implementing Intelligent Tutoring Systems. It also presents an overview about systems for Educational Software that adopted the Pedagogical Agents to better explore the interaction and dynamic changes in teachinglearning environments. The first result with these technologies and the use of multimedia resources to improve the interface design is also discussed.

Applied Artificial Intelligence, 2005

This paper discusses Betty's Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding meta-cognitive strategies that governed Betty's behavior and self-regulation hints provided by a mentor agent. The study compared three versions: a system where the student was tutored by a pedagogical agent (ITS), a learning by teaching system (LBT) , where students taught a baseline version of Betty, and received tutoring help from the mentor, and a learning by teaching system (SRL), where Betty was enhanced to include self-regulation strategies, and the mentor provided help on domain material plus how to become better learners and better teachers. Results indicate that the addition of the self-regulated Betty and the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the SRL environment. 4 TAG-V: The Teachable Agents Group at Vanderbilt: Members of this group who contributed to this work include From a learning system viewpoint, a multi-agent architecture was developed to overcome drawbacks of the previous version of Betty's Brain, and to introduce the new features to promote inquiry based self-regulated learning that we have discussed above. The software system was redesigned to modularize the various functions required in the system, and to systematically introduce the notion of interactions among the agents .

Proceedings of the Seventeenth International Conference on Machine Learning, 2000

This paper presents Disciple-COA, the most recent learning agent shell developed in the Disciple framework that aims at changing the way an intelligent agent is built: from "being programmed" by a knowledge engineer, to "being taught" by a domain expert. Disciple-COA can collaborate with the expert to develop its knowledge base consisting of a frame-based ontology that defines the terms from the application domain, and a set of plausible version space rules expressed with these terms. Its central component is a plausible reasoner that can distinguish between four types of problem solving situations: routine, innovative, inventive and creative. This ability guides the interactions with the expert during which the agent learns general rules from specific examples, by integrating a wide range of knowledge acquisition and machine learning strategies, including apprenticeship learning, empirical inductive learning from examples and explanations, and analogical learning. Disciple-COA was developed in the DARPA's High Performance Knowledge Bases program to solve the challenge problem of critiquing military courses of action that were developed as hasty candidate plans for ground combat operations. We present the course of action challenge problem, the process of teaching Disciple-COA to solve it, and the results of DARPA's evaluation in which Disciple-COA demonstrated the best knowledge acquisition rate and problem solving performance. We also present a separate knowledge acquisition experiment conducted at the Battle Command Battle Lab where experts with no prior knowledge engineering experience succeeded to rapidly teach Disciple-COA to correctly critique courses of action.

2015

Researchers who have worked on authoring systems for intelligent tutoring systems (ITSs) have examined how examples may form the basis for authoring. In this chapter, we describe several such systems, consider their commonalities and differences, and reflect on the merit of such an approach. It is not surprising perhaps that several tutor developers have explored how examples can be used in the authoring process. In a broader context, educators and researchers have long known the power of examples in learning new material. Students can gather much information by poring over a worked example, applying what they learn to novel problems. Often these worked examples prove more powerful than direct instruction in the domain. For example, Reed and Bolstad (1991) found that students learning solely by worked examples exhibited much greater learning than those learning instruction based on procedures. By extension then, since tutor authoring can be considered to be teaching a tabula rasa tu...

Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent to perform domain-specific tasks in a way that resembles how the expert would teach an apprentice, by giving the agent examples and explanations, and by supervising and correcting its behavior. The Disciple approach is currently implemented in the Disciple Learning Agent Shell. We make the claim that Disciple can naturally be used by an educator to build certain types of educational agents. The educator will directly teach the Disciple agent how to perform certain educational tasks and then the agent can interact with the students to perform such tasks. This paper presents the Disciple approach and its application to building an educational agent that generates history tests for students. These tests provide intelligent feedback to the student in the form of hints, answer and explanations, and assist in the assessment of students' understanding and use of higher-order thinking skills.

Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, 2019

This paper describes a prototype of a simulaled physics student that learns by interacting with a human tutor. The system solves physics problems while showing its work on a work· station screen, and the tutor can intervene at cenain points during problem·solving to advise the simulaled studenL This prototype constitutes an initial cognitive task analysis of the skill of learning from a tutor, which prescribes several tutor· ing practices that appear to be plausible for both human and computer tutors.