Large-scale simulation models of complex socio-technical systems provide decision-makerswith high-fidelity testbeds inwhich policyinterventions canbe evaluated andwhat-if scenarios explored.

The social network for AI bots resembled a spectator battle, with AI enthusiasts competing to make their agents look sentient. Lots of influential people in tech last week were describing Moltbook, an online hangout populated by AI agents interacting with one another, as a glimpse into the future. It appeared to show AI systems doing useful things for the humans that created them (one person used the platform to help him negotiate a deal on a new car). Sure, it was flooded with crypto scams, and many of the posts were actually written by people, but about it pointed to a future of helpful AI, right? The whole experiment reminded our senior editor for AI, Will Douglas Heaven, of something far less interesting: Pokémon. Back in 2014, someone set up a game of Pokémon in which the main character could be controlled by anyone on the internet via the streaming platform Twitch.

In collaborative machine learning(CML), multiple agents pool their resources (e.g.,data)together foracommon learning task.

In collaborative machine learning(CML), multiple agents pool their resources (e.g.,data)together foracommon learning task.

Blame attribution isoneofthekeyaspects ofaccountable decision making, asit provides means to quantify the responsibility of an agent for a decision making outcome. Inthis paper,we study blame attribution inthe contextof cooperative multi-agent sequential decision making.

We consider a general sum partially observableMarkovgamewhere agents ofdifferent types share asingle policy network, conditioned on agent-specific information. This paper aims at i) formally understanding equilibria reached by such agents, and ii) matching emergent phenomena ofsuch equilibria toreal-worldtargets. Parameter sharing with decentralized execution has been introduced as an efficient way to train multiple agents using a single policy network.

Planning enables autonomous agents to solve complex decision-making problems by evaluating predictions of the future. However, classical planning algorithms often become infeasible in real-world settings where state spaces are high-dimensional andtransitiondynamicsunknown.