Wanting to be Understood

• URL: http://arxiv.org/abs/2504.06611v2
• Date: Thu, 10 Apr 2025 07:46:00 GMT
• Title: Wanting to be Understood
• Authors: Chrisantha Fernando, Dylan Banarse, Simon Osindero,
• Abstract summary: This paper explores an intrinsic motivation for mutual awareness, hypothesizing that humans possess a fundamental drive to understand.<n>Through simulations of the perceptual crossing paradigm, we explore the effect of various internal reward functions in reinforcement learning agents.<n>Results indicate that while artificial curiosity alone does not lead to a preference for social interaction, rewards emphasizing reciprocal understanding successfully drive agents to prioritize interaction.
• Score: 7.40601112616244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores an intrinsic motivation for mutual awareness, hypothesizing that humans possess a fundamental drive to understand and to be understood even in the absence of extrinsic rewards. Through simulations of the perceptual crossing paradigm, we explore the effect of various internal reward functions in reinforcement learning agents. The drive to understand is implemented as an active inference type artificial curiosity reward, whereas the drive to be understood is implemented through intrinsic rewards for imitation, influence/impressionability, and sub-reaction time anticipation of the other. Results indicate that while artificial curiosity alone does not lead to a preference for social interaction, rewards emphasizing reciprocal understanding successfully drive agents to prioritize interaction. We demonstrate that this intrinsic motivation can facilitate cooperation in tasks where only one agent receives extrinsic reward for the behaviour of the other.

Related papers

• Emergence of Goal-Directed Behaviors via Active Inference with Self-Prior [4.863927022806184]
  Infants often exhibit goal-directed behaviors, such as reaching for a sensory stimulus, even when no external reward criterion is provided. We propose a novel density model for an agent's own multimodal sensory experiences, called the "self-prior" Our study implements intrinsically motivated behavior shaped by the agent's own sensory experiences, demonstrating the spontaneous emergence of intentional behavior during early development.
  arXiv  Detail & Related papers  (2025-04-15T11:16:27Z)
• Intrinsically-Motivated Humans and Agents in Open-World Exploration [50.00331050937369]
  We compare adults, children, and AI agents in a complex open-ended environment. We find that only Entropy and Empowerment are consistently positively correlated with human exploration progress.
  arXiv  Detail & Related papers  (2025-03-31T00:09:00Z)
• Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots [58.720142291102135]
  Humans have needs motivating their behavior according to intensity and context. We also create preferences associated with each action's perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context.
  arXiv  Detail & Related papers  (2023-02-20T04:52:24Z)
• Intrinsic Motivation in Dynamical Control Systems [5.635628182420597]
  We investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment. We show that this approach generalizes previous attempts to formalize intrinsic motivation. This opens the door for designing practical artificial, intrinsically motivated controllers.
  arXiv  Detail & Related papers  (2022-12-29T05:20:08Z)
• Complex behavior from intrinsic motivation to occupy action-state path space [0.0]
  We propose that the goal of behavior is maximizing future occupancy of paths of actions and states. According to this occupancy principle, rewards are the means to occupy path space, not the goal per se. We show that complex behaviors such as dancing', hide-and-seek and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space.
  arXiv  Detail & Related papers  (2022-05-20T17:32:41Z)
• Contrastive Active Inference [12.361539023886161]
  We propose a contrastive objective for active inference that reduces the computational burden in learning the agent's generative model and planning future actions. Our method performs notably better than likelihood-based active inference in image-based tasks, while also being computationally cheaper and easier to train.
  arXiv  Detail & Related papers  (2021-10-19T16:20:49Z)
• Mutual Information State Intrinsic Control [91.38627985733068]
  Intrinsically motivated RL attempts to remove this constraint by defining an intrinsic reward function. Motivated by the self-consciousness concept in psychology, we make a natural assumption that the agent knows what constitutes itself. We mathematically formalize this reward as the mutual information between the agent state and the surrounding state.
  arXiv  Detail & Related papers  (2021-03-15T03:03:36Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)
• Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions [3.420509295457138]
  This study aims to cover intrinsic motivation by taking an affective-engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors: expectation of capability and uncertainty. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation.
  arXiv  Detail & Related papers  (2020-10-13T14:23:57Z)
• Tracking Emotions: Intrinsic Motivation Grounded on Multi-Level Prediction Error Dynamics [68.8204255655161]
  We discuss how emotions arise when differences between expected and actual rates of progress towards a goal are experienced. We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals.
  arXiv  Detail & Related papers  (2020-07-29T06:53:13Z)
• Intrinsic Motivation for Encouraging Synergistic Behavior [55.10275467562764]
  We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.
  arXiv  Detail & Related papers  (2020-02-12T19:34:51Z)

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.