Think Before You Act: Popperian Expectations for Adaptive Agents
Bibliographic record
Abstract
Autonomous agents that operate and learn in complex environments must continually balance multiple objectives, including efficiency and risk. Trial-and-error reinforcement learning can reduce the efficiency and safety of agents due to the need to incorporate exploration along with the exploitation of learnt knowledge. Internal simulation frameworks, such as Winfield's consequence engine, have demonstrated the value of using internal simulations in agents to predict the outcomes of actions without requiring physical commitment. However, the framework lacks a formal mechanism for storing knowledge learned from these internal simulations, meaning agents are often stuck repeatedly simulating scenarios they have encountered previously. This paper introduces Popperian Expectations and a novel architecture that extends Winfield's framework by enabling agents to create, update, and utilise causal expectations learned from internal simulations. Using the Expectation Event Calculus (EEC), the agent can form interpretable causal expectations based on internal simulations. This paper explores how Popperian Expectations can be used to enable agents to reason reflectively and adapt to complex environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".