Cognitive science as a source of forward and inverse models of human decisions for robotics and control

• URL: http://arxiv.org/abs/2109.00127v1
• Date: Wed, 1 Sep 2021 00:28:28 GMT
• Title: Cognitive science as a source of forward and inverse models of human decisions for robotics and control
• Authors: Mark K. Ho and Thomas L. Griffiths
• Abstract summary: We look at how cognitive science can provide forward models of human decision-making. We highlight approaches that synthesize blackbox and theory-driven modeling. We aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.
• Score: 13.502912109138249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Those designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others' decision-making. We highlight relevant recent developments, including approaches that synthesize blackbox and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.

Related papers

• A Brain-inspired Computational Model for Human-like Concept Learning [12.737696613208632]
  The study develops a human-like computational model for concept learning based on spiking neural networks. By effectively addressing the challenges posed by diverse sources and imbalanced dimensionality of the two forms of concept representations, the study successfully attains human-like concept representations.
  arXiv  Detail & Related papers  (2024-01-12T09:32:51Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• Interpreting Neural Policies with Disentangled Tree Representations [58.769048492254555]
  We study interpretability of compact neural policies through the lens of disentangled representation. We leverage decision trees to obtain factors of variation for disentanglement in robot learning. We introduce interpretability metrics that measure disentanglement of learned neural dynamics.
  arXiv  Detail & Related papers  (2022-10-13T01:10:41Z)
• Modeling Human Behavior Part I -- Learning and Belief Approaches [0.0]
  We focus on techniques which learn a model or policy of behavior through exploration and feedback. Next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams.
  arXiv  Detail & Related papers  (2022-05-13T07:33:49Z)
• Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty [0.0]
  In Part I, we discussed methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning.
  arXiv  Detail & Related papers  (2022-05-13T07:29:15Z)
• 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)
• 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)
• Cognitive Perspectives on Context-based Decisions and Explanations [0.0]
  We show that the Contextual Importance and Utility method for XAI share an overlap with the current new wave of action-oriented predictive representational structures. This has an influencing effect on explainable AI, where the goal is to provide explanations of computer decision-making for a human audience.
  arXiv  Detail & Related papers  (2021-01-25T15:49:52Z)
• 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)

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.