Scott B. Huffman, John E. Laird
Situated, interactive tutorial intructions give flexibility in teaching tasks, by allowing communication of a variety of types of knowledge in a variety of situations. To exploit this flexibility, however, an instructable agent must be able to learn different types of knowledge from different instructional interactions. This paper presents an approach to learning from flexible tutorial instruction, called situated explanation, that takes advantage of constraints in different instructional contexts to guide the learning process. This makes it applicable to a wide range of instructional interactions. The theory is implemented in an agent called Instructo-Soar, that learns new tasks and other domain knowledge from natural language instructions. Instructo-Soar meets three key requirements of flexible instructability: it can (A) take any command at each instruction point, (B) handle instructions that apply to either the current situation or a hypothetical one (e.g., conditionals), and (C) 1 earn each type of knowledge it uses (derived from its underlying computational model) from instructions.