Interactively Learning a Blend of Goal-Based and Procedural Tasks

Agents that can learn new tasks through interactive instruction can utilize goal information to search for and learn flexible policies. This approach can be resilient to variations in initial conditions or issues that arise during execution. However, if a task is not easily formulated as achieving a goal or if the agent lacks sufficient domain knowledge for planning, other methods are required. We present a hybrid approach to interactive task learning that can learn both goal-oriented and procedural tasks, and mixtures of the two, from human natural language instruction. We describe this approach, go through two examples of learning tasks, and outline the space of tasks that the system can learn. We show that our approach can learn a variety of goal-oriented and procedural tasks from a single example and is robust to different amounts of domain knowledge

Learning Hierarchical Compositional Task Definitions through Online Situated Interactive Language Instruction

Artificial agents, from robots to personal assistants, have become competent workers in many settings and embodiments, but for the most part, they are limited to performing the capabilities and tasks with which they were initially programmed. Learning in these settings has predominately focused on learning to improve the agent’s performance on a task, and not on learning the actual definition of a task. The primary method for imbuing an agent with the task definition has been through programming by humans, who have detailed knowledge of the task, domain, and agent architecture. In contrast, humans quickly learn new tasks from scratch, often from instruction by another human. If we desire AI agents to be flexible and dynamically extendable, they will need to emulate these learning capabilities, and not be stuck with the limitation that task definitions must be acquired through programming. This dissertation explores the problem of how an Interactive Task Learning agent can learn the complete definition or formulation of novel tasks rapidly through online natural language instruction from a human instructor. Recent advances in natural language processing, memory systems, computer vision, spatial reasoning, robotics, and cognitive architectures make the time ripe to study how knowledge can be automatically acquired, represented, transferred, and operationalized. We present a learning approach embodied in an ITL agent that interactively learns the meaning of task concepts, the goals, actions, failure conditions, and task-specific terms, for 60 games and puzzles. In our approach, the agent learns hierarchical symbolic representations of task knowledge that enable it to transfer and compose knowledge, analyze and debug multiple interpretations, and communicate with the teacher to resolve ambiguity. Our results show that the agent can correctly generalize, disambiguate, and transfer concepts across variations of language descriptions and world representations, even with distractors present.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153434/1/jrkirk_1.pd