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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Interactive Grounding and Inference in Learning by Instruction.

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This summary is machine-generated.

This study introduces computational modeling constructs to enhance instruction following. These methods improve robustness by enabling flexible language grounding, inferring implicit knowledge, and dynamic clarification for adaptive learning and execution.

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Cognitive architecturesCognitive codeInstruction followingInteractive task learning

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Instruction following is a common learning form, with existing computational models showing success.
  • Current models often lack robustness and adaptability to instruction variations.

Purpose of the Study:

  • To present modeling constructs for more robust instruction following.
  • To address limitations in current computational models of instruction following.

Main Methods:

  • Developing flexible language-to-execution grounding.
  • Implementing instruction processing for implicit knowledge inference.
  • Enabling dynamic, interactive clarification during learning and execution.

Main Results:

  • Proposed constructs enhance the adaptability and robustness of instruction-following models.
  • Demonstrated effectiveness in paired-associates and visual-search tasks.

Conclusions:

  • The developed modeling constructs offer significant improvements for computational instruction following.
  • These advancements pave the way for more flexible and intelligent learning systems.