The challenge addressed here is that of ensuring that all applicable rules, laws, and regulations are loaded into artificial-intelligence agents' working memories as they encounter wide, potentially open-ended, sets of situations.

By agents being able to search for, retrieve, and load applicable rules, laws, and regulations into their working memories, they could be in alignment with these items and subsequently select actions in accordance with them.

Conversational search engines for rules, laws, and regulations could, through dialogue, ask questions about narrated situations to better retrieve applicable search results. Through dialogue, search results could be accompanied by explanation or argumentation connecting them to input situations.

The goal of artificial-intelligence alignment is to ensure that artificial intelligence systems are properly aligned with human values (Gabriel, 2020).

This goal can be phrased as: (1) agents doing what they are instructed to do, (2) agents doing what they are intended to do, (3) agents doing what humans' behavior reveals them to prefer, (4) agents doing what humans would, if rational and informed, want them to do, (5) agents doing what is in the best interests of humans, objectively, or (6) agents doing what they morally ought, as defined by human individuals and society.

"If law is leveraged as a set of methodologies for conveying and interpreting directives and a knowledge base of societal values, it can play a unique role in aligning AI with humans" (Nay, 2022).

Agents are expected to comply with rules, laws, and regulations. The number of rules, laws, and regulations is expected to be large. For each rule, law, and regulation, for each considered action, an agent is expected to verify that that action is in compliance. Ideally, agents will be able to act in real-time while performing these computations.

"In any given matter, before legal reasoning can take place, the reasoning agent must first engage in a task of 'law search' to identify the legal knowledge – cases, statutes, or regulations – that bear on the questions being addressed. This task may seem straightforward or obvious, but upon inspection, it presents difficult problems of definition and is challenging to represent in a tractable formalization that can be computationally executed" (Dadgostari, Guim, Beling, Livermore, & Rockmore, 2021).

Legal search engines could be of use for agents to search for and retrieve those rules, laws, and regulations applicable to their internal states, world models, and working memory contents.

Conversational search engines for rules, laws, and regulations could interface as agents participating in multi-agent systems.

Agents' internal states, world models, and working memory contents and the transcripts from multi-agent systems' dialogues could be inspected to determine whether applicable rules, laws, and regulations were properly loaded and available.

Techniques for inducing agents' internal states, world models, and working memory contents include narration, simulation, replay, and other forms of direct or manual induction.

Event logs involving agents' internal states, world models, and working memories could be created, perhaps accompanying multi-agent transcripts or recordings of environments.

In the future, these development and operations processes of inducing contexts and verifying that applicable rules, laws, and regulations are loaded into agents could be increasingly computer-aided or automated.

Dadgostari, Faraz, Mauricio Guim, Peter A. Beling, Michael A. Livermore, and Daniel N. Rockmore. "Modeling law search as prediction." Artificial Intelligence and Law 29 (2021): 3-34.

Gabriel, Iason. "Artificial intelligence, values, and alignment." Minds and Machines 30, no. 3 (2020): 411-437.

Nay, John J. "Law informs code: A legal informatics approach to aligning artificial intelligence with humans." Northwestern Journal of Technology and Intellectual Property 20 (2022): 309.