A Computational Model for Situated Task Learning with Interactive Instruction
This work addresses the challenge of situated task learning for cognitive architectures, but it is incremental as it builds on prior ACT-R models by extending to interactive instruction.
The paper tackles the problem of learning novel tasks from situated interactive instruction by developing a computational model in the Soar cognitive architecture, evaluating its ability to acquire both declarative and procedural knowledge through task-oriented interactions with an expert.
Learning novel tasks is a complex cognitive activity requiring the learner to acquire diverse declarative and procedural knowledge. Prior ACT-R models of acquiring task knowledge from instruction focused on learning procedural knowledge from declarative instructions encoded in semantic memory. In this paper, we identify the requirements for designing compu- tational models that learn task knowledge from situated task- oriented interactions with an expert and then describe and evaluate a model of learning from situated interactive instruc- tion that is implemented in the Soar cognitive architecture.