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Aaron Mininger

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2022 System Paper

A Demonstration of Compositional, Hierarchical Interactive Task Learning

Aaron Mininger
John E. Laird

We present a demonstration of the interactive task learning agent Rosie, where it learns the task of patrolling a simulated barracks environment through situated natural language instruction. In doing so, it builds a sizable task hierarchy composed of both innate and learned tasks, tasks formulated as achieving a goal or following a procedure, tasks with conditional branches and loops, and involving communicative and mental actions. Rosie is implemented in the Soar cognitive architecture, and represents tasks using a declarative task network which it compiles into procedural rules through chunking. This is key to allowing it to learn from a single training episode and generalize quickly.

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AAAI Conference 2018 Conference Paper

Interactively Learning a Blend of Goal-Based and Procedural Tasks

Aaron Mininger
John Laird

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.

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IJCAI Conference 2016 Conference Paper

A Demonstration of Interactive Task Learning

James Kirk
Aaron Mininger
John Laird

We will demonstrate a tabletop robotic agent that learns new tasks through interactive natural language instruction. The tasks to be demonstrated are simple puzzles and games, such as Tower of Hanoi, Eight Puzzle, Tic-Tac-Toe, Three Men's Morris, and the Frog and Toads puzzle. We will include a live, interactive simulation of a mobile robot that learns new tasks using the same system.

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