Teachable agents

Definition

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Programming intelligent agents requires several important processes: 1) defining what the agent needs to know 2) defining a representation of this knowledge 3) and programming this knowledge into the agent. This process of evaluating the needs of the agent to perform a task can lead to deeper understanding of the domain the agent needs to know about.

• Brophy S., Schwartz, D., Biswas, G., & Bransford, J. (1998, August). Learning Through Programmable Agents. Presented at Workshop on Pedagogical Agents, ITS '98, San Antonio, TX, August 1998.

Intelligent agents

Intelligent agents possess qualities that make them autonomous entities that can exist in complex, dynamic, and open environments such as the Internet. An agent can "sense, and act on, its environment, and has a set of goals or motivations that it tries to achieve through these actions." (Maes, 1997). Research in the area of intelligent agents focuses on questions like "How does an agent make an appropriate decision; How does an agent learn; How does it adapt." In this context, learning focuses on algorithms designed to maintain an agents' autonomy and perform the desired goals of the person employing the agent.

Previous work in learning by programming and the metaphor of computer agents provides insights toward creating opportunities that may yeild benefits similar to those of learning to those of learning by teaching. Within the domain of computer technology, the idea of learning by teaching was emphasized by Papert (1980) in the context of helping students learn logo by teaching the "turtle" (see also Abelson & diSessa, 1980; Mayer, 1988; Salomon, 1992). Extensions of this idea include programming lego toys and robots to interact with one another explicitly (e.g., Kafai & Resnick, 1996; Repenning & Sumner, 1995), programming computer agents to collaboratively learn from one another (e.g., Dillenbourg, in press), creating micro worlds, and creating software to help others learn topics such as mathematics (e.g., Harel & Papert, 1991).

Use in ID

A designer must evaluate  how  an agent acts and how it  interacts with other agents (dependencies) in the simulation environment.  The designer must translate their hypothesis into an agent's actions/interactions using a visual programming environment to program sets of  propositions defining the behavior of the agents.  To create these propositions, users identify conditions when specific actions should be taken by agents, and they can specify actions using simple click and drag  techniques.  Then the designer can evaluate the design by "running" the simulation to evaluate whether they correctly modelled the desired outcome.  Used as a learning environment the designers are learners trying to  model the dynamics of a complex situations.  This inquiry process of evaluation, implement and test provides an excellent learning opportunity that requires very little programming experience.