Goals and the Structure of Experience

2025-08-20

AI Summary

Traditional models treat descriptive (world-state) and normative (value-preference) representations as disjoint, hindering unified accounts of goal-directed behavior. Method: Inspired by Buddhist epistemology, we introduce “teleological states”—a novel construct grounded in the experience distribution induced by an agent’s goal—unifying behavioral, phenomenal, and neural levels. We develop a computational framework where descriptive and normative world models co-emerge from a single goal through environmental interaction, using statistical analysis of experience sequences to define state representations driven by discrepancies between realized and ideal experience features. Contribution/Results: This framework formally demonstrates, for the first time, the joint emergence of descriptive and normative models from shared teleological foundations. It transcends the conventional reinforcement learning separation of state and reward, offering a unified, multi-level modeling paradigm applicable to both natural and artificial goal-directed systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Purposeful behavior is a hallmark of natural and artificial intelligence. Its acquisition is often believed to rely on world models, comprising both descriptive (what is) and prescriptive (what is desirable) aspects that identify and evaluate state of affairs in the world, respectively. Canonical computational accounts of purposeful behavior, such as reinforcement learning, posit distinct components of a world model comprising a state representation (descriptive aspect) and a reward function (prescriptive aspect). However, an alternative possibility, which has not yet been computationally formulated, is that these two aspects instead co-emerge interdependently from an agent's goal. Here, we describe a computational framework of goal-directed state representation in cognitive agents, in which the descriptive and prescriptive aspects of a world model co-emerge from agent-environment interaction sequences, or experiences. Drawing on Buddhist epistemology, we introduce a construct of goal-directed, or telic, states, defined as classes of goal-equivalent experience distributions. Telic states provide a parsimonious account of goal-directed learning in terms of the statistical divergence between behavioral policies and desirable experience features. We review empirical and theoretical literature supporting this novel perspective and discuss its potential to provide a unified account of behavioral, phenomenological and neural dimensions of purposeful behaviors across diverse substrates.

Problem

• Co-emergence of descriptive and prescriptive world model aspects
• Computational framework for goal-directed state representation
• Unified account of behavioral and neural purposeful behaviors

Innovation

• Goal-directed state representation framework
• Co-emergence of descriptive and prescriptive aspects
• Telic states based on goal-equivalent experience distributions