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Learning intermediate goals and methods from situated instructions

Despite many recent advances in knowledge acquisition tools and techniques, acquiring knowledge is still a major bottleneck in building intelligent systems for complex domains. Typically, a team of knowledge engineers extensively interviews domain experts, and then manually translates the information obtained into a form that the intelligent system can use. This process is costly and time consuming. In addition, all of the expert knowledge must be put into the system a-priori; after the system is running, human expertise cannot be used to change or add knowledge (short of turning off the system and reprogramming it). In this work, we are exploring an alternative: learning domain knowledge from situated instruction given by the domain expert. In situated instruction, an instructor describes and explains appropriate goals and actions in specific situations either the current situation, or one set up by the instructor. From these specific instructions, the system will learn general knowledge about goals and methods that can be used for planning and acting in the domain. Instruction allows the system to take advantage of human expertise during its ongoing operation. We believe that instruction is a potentially powerful knowledge source for systems learning to plan and act in complex domains. From instruction, a system can learn completely new goals and methods [Huffman, 1994]. Because of the large space of possible goals and methods in complex domains, it would be difficult for a system to "discover" many of the useful goals and methods without human guidance. Indeed, learning techniques that do not involve human guidance, like exploration and experimentation in the environment (e.g., [Gil, 1992; Shen and Simon, 1989]) have concentrated on revising knowledge about domain operators, rather than discovering new performance goals (although LIVE [Shen and Simon, 1989] can discover hidden features in its environment that affect performance). This paper describes techniques we have developed for learning intermediate goals and associated methods from interactive natural language instructions. The techniques are implemented in an agent called Instructo-Soar [Huffman, 1994; Huffman and Laird, 1994]. To date, Instructo-Soar has been applied in depth only to a small, simulated robotic domain (not a "real" domain). Truly flexible instructability in real domains makes a number of difficult demands on an instructable agent, that we have enumerated elsewhere [Huffman, 1994]. In this paper, after briefly illustrating Instructo-Soar’s techniques with examples from its toy domain, we turn to a protocol of human instruction in a real domain (tactical air combat). This protocol used to highlight a number of extensions required to move our instructable agent closer to real domains.