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Daniel Jarne Ornia

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Emergent Risk Awareness in Rational Agents under Resource Constraints

Daniel Jarne Ornia
Nicholas Bishop
Joel Dyer
Wei-Chen Lee
Anisoara Calinescu
Doyne Farmer
Michael Wooldridge

Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision‑making problems under (often approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade‑offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a human principal to act on their behalf, asymmetries in constraint exposure can give rise to previously unanticipated misalignment between human objectives and agent incentives. We formalise this setting through a survival bandit framework, provide theoretical and empirical results that quantify the impact of survival‑driven preference shifts, identify conditions under which misalignment emerges and propose mechanisms to mitigate the emergence of risk-seeking or risk-averse behaviours. As a result, this work aims to increase understanding and interpretability of emergent behaviours of AI agents operating under such survival pressure, and offer guidelines for safely deploying such AI systems in critical resource‑limited environments.

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ICML Conference 2025 Conference Paper

Learning Likelihood-Free Reference Priors

Nicholas Bishop
Daniel Jarne Ornia
Joel Dyer
Anisoara Calinescu
Michael J. Wooldridge

Simulation modeling offers a flexible approach to constructing high-fidelity synthetic representations of complex real-world systems. However, the increased complexity of such models introduces additional complications, for example when carrying out statistical inference procedures. This has motivated a large and growing literature on likelihood-free or simulation-based inference methods, which approximate (e. g. , Bayesian) inference without assuming access to the simulator’s intractable likelihood function. A hitherto neglected problem in the simulation-based Bayesian inference literature is the challenge of constructing minimally informative reference priors for complex simulation models. Such priors maximise an expected Kullback-Leibler distance from the prior to the posterior, thereby influencing posterior inferences minimally and enabling an “objective” approach to Bayesian inference that does not necessitate the incorporation of strong subjective prior beliefs. In this paper, we propose and test a selection of likelihood-free methods for learning reference priors for simulation models, using variational approximations to these priors and a variety of mutual information estimators. Our experiments demonstrate that good approximations to reference priors for simulation models are in this way attainable, providing a first step towards the development of likelihood-free objective Bayesian inference procedures.

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AAMAS Conference 2025 Conference Paper

Predictability Awareness for Efficient and Robust Multi-Agent Coordination

Román Chiva Gil
Daniel Jarne Ornia
Khaled A. Mustafa
Javier Alonso Mora

To safely and efficiently solve motion planning problems in multiagent settings, most approaches attempt to solve a joint optimization that explicitly accounts for the responses triggered in other agents. This often results in solutions with an exponential computational complexity, making these methods intractable for complex scenarios with many agents. While sequential predict-and-plan approaches are more scalable, they tend to perform poorly in highly interactive environments. This paper proposes a method to improve the interactive capabilities of sequential predict-and-plan methods in multi-agent navigation problems by introducing predictability as an optimization objective. We interpret predictability through the use of general prediction models, by allowing agents to predict themselves and estimate how they align with these external predictions. We formally introduce this behavior through the freeenergy of the system, which reduces (under appropriate bounds) to the Kullback-Leibler divergence between plan and prediction, and use this as a penalty for unpredictable trajectories. The proposed interpretation of predictability allows agents to more robustly leverage prediction models, and fosters a ‘soft social convention’ that accelerates agreement on coordination strategies without the need of explicit high level control or communication. We show how this predictability-aware planning leads to lower-cost trajectories and reduces planning effort in a set of multi-robot problems, including autonomous driving experiments with human driver data, where we show that the benefits of considering predictability apply even when only the ego-agent uses this strategy. The code and experiment videos can be found in the following page: https: //romanchiva. github. io/PAProjectPage/

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TMLR Journal 2025 Journal Article

Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization

Daniel Jarne Ornia
Giannis Delimpaltadakis
Jens Kober
Javier Alonso-Mora

In Reinforcement Learning (RL), agents have no incentive to exhibit predictable trajectories, and are often pushed (through e.g. policy entropy regularisation) to randomise their actions in favor of exploration. This lack of predictability awareness often makes it challenging for other agents and humans to predict an agent's trajectories, possibly triggering unsafe scenarios (e.g. in human-robot interaction). We propose a novel method to induce predictable trajectories in RL agents, termed Predictability-Aware RL (PARL), employing the agent's trajectory entropy rate to quantify predictability. Our method maximizes a linear combination of a standard discounted reward and the negative entropy rate, thus trading off optimality with predictability. We show how the entropy rate can be formally cast as an average reward, how entropy-rate value functions can be estimated from a learned model and incorporate this in policy-gradient algorithms, and demonstrate how this approach produces predictable (near-optimal) policies in tasks inspired by human-robot use-cases.

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