KIX: A Metacognitive Generalization Framework
- URL: http://arxiv.org/abs/2402.05346v1
- Date: Thu, 8 Feb 2024 01:41:28 GMT
- Title: KIX: A Metacognitive Generalization Framework
- Authors: Arun Kumar, Paul Schrater
- Abstract summary: We present a metacognitive generalization framework, Knowledge-Interaction-eXecution (KIX)
We argue that interactions with objects leveraging type space facilitate the learning of transferable interaction concepts and generalization.
It is a natural way of integrating knowledge into reinforcement learning and promising to act as an enabler for autonomous and generalist behaviors in artificial intelligence systems.
- Score: 2.860579409350583
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Humans and other animals aptly exhibit general intelligence behaviors in
solving a variety of tasks with flexibility and ability to adapt to novel
situations by reusing and applying high level knowledge acquired over time. But
artificial agents are more of a specialist, lacking such generalist behaviors.
Artificial agents will require understanding and exploiting critical structured
knowledge representations. We present a metacognitive generalization framework,
Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects
leveraging type space facilitate the learning of transferable interaction
concepts and generalization. It is a natural way of integrating knowledge into
reinforcement learning and promising to act as an enabler for autonomous and
generalist behaviors in artificial intelligence systems.
Related papers
- Improving deep learning with prior knowledge and cognitive models: A
survey on enhancing explainability, adversarial robustness and zero-shot
learning [0.0]
We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses.
Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots.
arXiv Detail & Related papers (2024-03-11T18:11:00Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Incremental procedural and sensorimotor learning in cognitive humanoid
robots [52.77024349608834]
This work presents a cognitive agent that can learn procedures incrementally.
We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent.
Results show that this approach is capable of solving complex tasks incrementally.
arXiv Detail & Related papers (2023-04-30T22:51:31Z) - Intrinsically Motivated Learning of Causal World Models [0.0]
A promising direction is to build world models capturing the true physical mechanisms hidden behind the sensorimotor interaction with the environment.
Inferring the causal structure of the environment could benefit from well-chosen actions as means to collect relevant interventional data.
arXiv Detail & Related papers (2022-08-09T16:48:28Z) - HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem
Solving [104.79156980475686]
Humans learn compositional and causal abstraction, ie, knowledge, in response to the structure of naturalistic tasks.
We argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic.
This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving.
arXiv Detail & Related papers (2021-02-22T20:37:01Z) - Hierarchical principles of embodied reinforcement learning: A review [11.613306236691427]
We show that all important cognitive mechanisms have been implemented independently in isolated computational architectures.
We expect our results to guide the development of more sophisticated cognitively inspired hierarchical methods.
arXiv Detail & Related papers (2020-12-18T10:19:38Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.