An Affective-Taxis Hypothesis for Alignment and Interpretability

• URL: http://arxiv.org/abs/2505.17024v1
• Date: Sat, 03 May 2025 04:49:01 GMT
• Title: An Affective-Taxis Hypothesis for Alignment and Interpretability
• Abstract summary: AI alignment aims to ensure that agents always behave in a manner aligned with the goals and values of their human operators.<n>This paper proposes an affectivist approach to the alignment problem, re-framing the concepts of goals and values in terms of affective taxis.<n>We conclude with a discussion of the role of affective taxis in AI alignment.
• Score: 0.9471093245585006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: AI alignment is a field of research that aims to develop methods to ensure that agents always behave in a manner aligned with (i.e. consistently with) the goals and values of their human operators, no matter their level of capability. This paper proposes an affectivist approach to the alignment problem, re-framing the concepts of goals and values in terms of affective taxis, and explaining the emergence of affective valence by appealing to recent work in evolutionary-developmental and computational neuroscience. We review the state of the art and, building on this work, we propose a computational model of affect based on taxis navigation. We discuss evidence in a tractable model organism that our model reflects aspects of biological taxis navigation. We conclude with a discussion of the role of affective taxis in AI alignment.

Related papers

• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
  This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
  arXiv  Detail & Related papers  (2025-05-13T17:59:20Z)
• TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility [11.436186697804835]
  Trajectory prediction is crucial for autonomous driving, enabling vehicles to anticipate the movements of surrounding road users.<n>Current deep learning models often lack trustworthiness as their predictions can be physically infeasible and illogical to humans.<n>We propose incorporating interaction and kinematic priors of all agent classes to capture agent behavioral differences.
  arXiv  Detail & Related papers  (2025-05-10T19:29:32Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles [20.123965317836106]
  Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of research. It focuses on the development of autonomous vehicles that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the autonomous vehicle to be able to understand and predict the behaviour of human road users.
  arXiv  Detail & Related papers  (2023-10-29T03:43:50Z)
• The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
  We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction. We discuss the implications, strengths, and limitations of different integration principles.
  arXiv  Detail & Related papers  (2023-08-10T17:53:03Z)
• Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps [6.99274104609965]
  Motion is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs) We propose a novel framework that combines deep-temporal and probabilistic approaches to predict vehicle behaviors.
  arXiv  Detail & Related papers  (2023-08-08T14:49:44Z)
• Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction [29.701029725302586]
  We study the joint trajectory prediction problem with the goal-conditioned framework. We introduce a conditional-variational-autoencoder-based (CVAE) model to explicitly encode different interaction modes into the latent space. We propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
  arXiv  Detail & Related papers  (2022-03-28T21:41:21Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving [47.22329993674051]
  We aim to develop an explainable model that generates explanations consistent with both human domain knowledge and the model's inherent causal relation. In particular, we focus on an essential building block of autonomous driving, multi-agent interaction modeling. We demonstrate that it can model interactive traffic scenarios under both simulation and real-world settings.
  arXiv  Detail & Related papers  (2021-02-23T19:34:32Z)

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.