#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to study normative behaviour

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For many people, synthetic intelligence (AI) has turn into a part of on a regular basis life, and the speed at which we assign beforehand human roles to AI methods reveals no indicators of slowing down. AI methods are the essential substances of many applied sciences — e.g., self-driving automobiles, sensible city planning, digital assistants — throughout a rising variety of domains. At the core of many of those applied sciences are autonomous brokers — methods designed to behave on behalf of people and make choices with out direct supervision. In order to behave successfully in the actual world, these brokers have to be able to finishing up a variety of duties regardless of probably unpredictable environmental situations, which frequently requires some type of machine studying (ML) for attaining adaptive behaviour.

Reinforcement studying (RL) [6] stands out as a robust ML method for coaching brokers to realize optimum behaviour in stochastic environments. RL brokers study by interacting with their setting: for each motion they take, they obtain context-specific rewards or penalties. Over time, they study behaviour that maximizes the anticipated rewards all through their runtime.

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RL brokers can grasp all kinds of advanced duties, from profitable video video games to controlling cyber-physical methods similar to self-driving automobiles, typically surpassing what knowledgeable people are able to. This optimum, environment friendly behaviour, nonetheless, if left totally unconstrained, might transform off-putting and even harmful to the people it impacts. This motivates the substantial analysis effort in secure RL, the place specialised strategies are developed to make sure that RL brokers meet particular security necessities. These necessities are sometimes expressed in formal languages like linear temporal logic (LTL), which extends classical (true/false) logic with temporal operators, permitting us to specify situations like “something that must always hold”, or “something that must eventually occur”. By combining the adaptability of ML with the precision of logic, researchers have developed highly effective strategies for coaching brokers to behave each successfully and safely.

However, security isn’t every little thing. Indeed, as RL-based brokers are more and more given roles that both exchange or intently work together with people, a brand new problem arises: guaranteeing their conduct can be compliant with the social, authorized and moral norms that construction human society, which frequently transcend easy constraints guaranteeing security. For instance, a self-driving automotive may completely comply with security constraints (e.g. avoiding collisions), but nonetheless undertake behaviors that, whereas technically secure, violate social norms, showing weird or impolite on the highway, which could trigger different (human) drivers to react in unsafe methods.

Norms are usually expressed as obligations (“you must do it”), permissions (“you are permitted to do it”) and prohibitions (“you are forbidden from doing it”), which aren’t statements that may be true or false, like classical logic formulation. Instead, they’re deontic ideas: they describe what is true, fallacious, or permissible — ideally suited or acceptable behaviour, as an alternative of what’s really the case. This nuance introduces a number of tough dynamics to reasoning about norms, which many logics (similar to LTL) wrestle to deal with. Even every-day normative methods like driving rules can function such problems; whereas some norms will be quite simple (e.g., by no means exceed 50 kph inside metropolis limits), others will be extra advanced, as in:

1. Always preserve 10 meters between your automobile and the autos in entrance of and behind you.
2. If there are lower than 10 meters between you and the automobile behind you, you need to decelerate to place more room between your self and the automobile in entrance of you.

(2) is an instance of a contrary-to-duty obligation (CTD), an obligation you need to comply with particularly in a state of affairs the place one other major obligation (1) has already been violated to, e.g., compensate or scale back harm. Although studied extensively within the fields of normative reasoning and deontic logic, such norms will be problematic for a lot of primary secure RL strategies based mostly on imposing LTL constraints, as was mentioned in [4].

However, there are approaches for secure RL that present extra potential. One notable instance is the Restraining Bolt method, launched by De Giacomo et al. [2]. Named after a tool used within the Star Wars universe to curb the conduct of droids, this methodology influences an agent’s actions to align with specified guidelines whereas nonetheless permitting it to pursue its targets. That is, the restraining bolt modifies the conduct an RL agent learns in order that it additionally respects a set of specs. These specs, expressed in a variant of LTL (LTLf [3]), are every paired with its personal reward. The central concept is straightforward however highly effective: together with the rewards the agent receives whereas exploring the setting, we add an extra reward each time its actions fulfill the corresponding specification, nudging it to behave in ways in which align with particular person security necessities. The task of particular rewards to particular person specs permits us to mannequin extra difficult dynamics like, e.g., CTD obligations, by assigning one reward for obeying the first obligation, and a distinct reward for obeying the CTD obligation.

Still, points with modeling norms persist; for instance, many (if not most) norms are conditional. Consider the duty stating “if pedestrians are present at a pedestrian crossing, THEN the nearby vehicles must stop”. If an agent had been rewarded each time this rule was glad, it could additionally obtain rewards in conditions the place the norm is just not really in pressure. This is as a result of, in logic, an implication holds additionally when the antecedent (“pedestrians are present”) is fake. As a outcome, the agent is rewarded each time pedestrians are usually not round, and may study to lengthen its runtime with the intention to accumulate these rewards for successfully doing nothing, as an alternative of effectively pursuing its meant job (e.g., reaching a vacation spot). In [5] we confirmed that there are situations the place an agent will both ignore the norms, or study this “procrastination” conduct, regardless of which rewards we select. As a outcome, we launched Normative Restraining Bolts (NRBs), a step ahead towards imposing norms in RL brokers. Unlike the unique Restraining Bolt, which inspired compliance by offering extra rewards, the normative model as an alternative punishes norm violations. This design is impressed by the Andersonian view of deontic logic [1], which treats obligations as guidelines whose violation essentially triggers a sanction. Thus, the framework now not depends on reinforcing acceptable conduct, however as an alternative enforces norms by guaranteeing that violations carry tangible penalties. While efficient for managing intricate normative dynamics like conditional obligations, contrary-to-duties, and exceptions to norms, NRBs depend on trial-and-error reward tuning to implement norm adherence, and due to this fact will be unwieldy, particularly when making an attempt to resolve conflicts between norms. Moreover, they require retraining to accommodate norm updates, and don’t lend themselves to ensures that optimum insurance policies decrease norm violations.

Our contribution

Building on NRBs, we introduce Ordered Normative Restraining Bolts (ONRBs), a framework for guiding reinforcement studying brokers to adjust to social, authorized, and moral norms whereas addressing the constraints of NRBs. In this strategy, every norm is handled as an goal in a multi-objective reinforcement studying (MORL) drawback. Reformulating the issue on this method permits us to:

• Prove that when norms don’t battle, an agent who learns optimum behaviour will decrease norm violations over time.
• Express relationships between norms by way of a rating system describing which norm ought to be prioritized when a battle happens.
• Use MORL strategies to algorithmically decide the required magnitude of the punishments we assign such that it’s guarantied that as long as an agent learns optimum behaviour, norms will probably be violated as little as potential, prioritizing the norms with the very best rank.
• Accommodate adjustments in our normative methods by “deactivating” or “reactivating” particular norms.

We examined our framework in a grid-world setting impressed by technique video games, the place an agent learns to gather assets and ship them to designated areas. This setup permits us to show the framework’s skill to deal with the advanced normative situations we famous above, together with direct prioritization of conflicting norms and norm updates. For occasion, the determine under

shows how the agent handles norm conflicts, when it’s each obligated to (1) keep away from the damaging (pink) areas, and (2) attain the market (blue) space by a sure deadline, supposing that the second norm takes precedence. We can see that it chooses to violate (1) as soon as, as a result of in any other case it is going to be caught firstly of the map, unable to satisfy (2). Nevertheless, when given the likelihood to violate (1) as soon as extra, it chooses the compliant path, though the violating path would enable it to gather extra assets, and due to this fact extra rewards from the setting.

In abstract, by combining RL with logic, we will construct AI brokers that don’t simply work, they work proper.

This work received a distinguished paper award at IJCAI 2025. Read the paper in full: Combining MORL with restraining bolts to study normative behaviour, Emery A. Neufeld, Agata Ciabattoni and Radu Florin Tulcan.

Acknowledgements

This analysis was funded by the Vienna Science and Technology Fund (WWTF) venture ICT22-023 and the Austrian Science Fund (FWF) 10.55776/COE12 Cluster of Excellence Bilateral AI.

References

[1] Alan Ross Anderson. A discount of deontic logic to alethic modal logic. Mind, 67(265):100–103, 1958.

[2] Giuseppe De Giacomo, Luca Iocchi, Marco Favorito, and Fabio Patrizi. Foundations for restraining bolts: Reinforcement studying with LTLf/LDLf restraining specs. In Proceedings of the worldwide convention on automated planning and scheduling, quantity 29, pages 128–136, 2019.

[3] Giuseppe De Giacomo and Moshe Y Vardi. Linear temporal logic and linear dynamic logic on finite traces. In IJCAI, quantity 13, pages 854–860, 2013.

[4] Emery Neufeld, Ezio Bartocci, and Agata Ciabattoni. On normative reinforcement studying by way of secure reinforcement studying. In PRIMA 2022, 2022.

[5] Emery A Neufeld, Agata Ciabattoni, and Radu Florin Tulcan. Norm compliance in reinforcement studying brokers by way of restraining bolts. In Legal Knowledge and Information Systems JURIX 2024, pages 119–130. IOS Press, 2024.

[6] Richard S. Sutton and Andrew G. Barto. Reinforcement studying – an introduction. Adaptive computation and machine studying. MIT Press, 1998.