Explainable Goal-Driven Agents and Robots -- A Comprehensive Review
- URL: http://arxiv.org/abs/2004.09705v9
- Date: Fri, 23 Sep 2022 08:52:58 GMT
- Title: Explainable Goal-Driven Agents and Robots -- A Comprehensive Review
- Authors: Fatai Sado, Chu Kiong Loo, Wei Shiung Liew, Matthias Kerzel, Stefan
Wermter
- Abstract summary: The paper reviews approaches on explainable goal-driven intelligent agents and robots.
It focuses on techniques for explaining and communicating agents perceptual functions and cognitive reasoning.
It suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots.
- Score: 13.94373363822037
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent applications of autonomous agents and robots, such as self-driving
cars, scenario-based trainers, exploration robots, and service robots have
brought attention to crucial trust-related challenges associated with the
current generation of artificial intelligence (AI) systems. AI systems based on
the connectionist deep learning neural network approach lack capabilities of
explaining their decisions and actions to others, despite their great
successes. Without symbolic interpretation capabilities, they are black boxes,
which renders their decisions or actions opaque, making it difficult to trust
them in safety-critical applications. The recent stance on the explainability
of AI systems has witnessed several approaches on eXplainable Artificial
Intelligence (XAI); however, most of the studies have focused on data-driven
XAI systems applied in computational sciences. Studies addressing the
increasingly pervasive goal-driven agents and robots are still missing. This
paper reviews approaches on explainable goal-driven intelligent agents and
robots, focusing on techniques for explaining and communicating agents
perceptual functions (example, senses, and vision) and cognitive reasoning
(example, beliefs, desires, intention, plans, and goals) with humans in the
loop. The review highlights key strategies that emphasize transparency,
understandability, and continual learning for explainability. Finally, the
paper presents requirements for explainability and suggests a roadmap for the
possible realization of effective goal-driven explainable agents and robots.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge.
We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
arXiv Detail & Related papers (2024-10-31T17:22:30Z) - Explaining Explaining [0.882727051273924]
Explanation is key to people having confidence in high-stakes AI systems.
Machine-learning-based systems can't explain because they are usually black boxes.
We describe a hybrid approach to developing cognitive agents.
arXiv Detail & Related papers (2024-09-26T16:55:44Z) - Trustworthy Conceptual Explanations for Neural Networks in Robot Decision-Making [9.002659157558645]
We introduce a trustworthy explainable robotics technique based on human-interpretable, high-level concepts.
Our proposed technique provides explanations with associated uncertainty scores by matching neural network's activations with human-interpretable visualizations.
arXiv Detail & Related papers (2024-09-16T21:11:12Z) - Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces.
While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks.
This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
arXiv Detail & Related papers (2024-04-29T04:51:05Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - 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) - Towards Reconciling Usability and Usefulness of Explainable AI
Methodologies [2.715884199292287]
Black-box AI systems can lead to liability and accountability issues when they produce an incorrect decision.
Explainable AI (XAI) seeks to bridge the knowledge gap, between developers and end-users.
arXiv Detail & Related papers (2023-01-13T01:08:49Z) - Evaluating Human-like Explanations for Robot Actions in Reinforcement
Learning Scenarios [1.671353192305391]
We make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action.
These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods.
arXiv Detail & Related papers (2022-07-07T10:40:24Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Integrating Intrinsic and Extrinsic Explainability: The Relevance of
Understanding Neural Networks for Human-Robot Interaction [19.844084722919764]
Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems.
NICO, an open-source humanoid robot platform, is introduced and how the interaction of intrinsic explanations by the robot itself and extrinsic explanations provided by the environment enable efficient robotic behavior.
arXiv Detail & Related papers (2020-10-09T14:28:48Z)
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.