arXiv:2607. 22750v1 Announce Type: new Abstract: As AI agents operate with increasing autonomy in a multi-agent world, they will need to learn to cooperate with other agents and with humans to generate mutual benefits.
arXiv:2602. 23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts.
The paper discusses the agent-centric general value function (ACGVF) framework, which allows an agent to decide both which goal to pursue and when to consider a goal finished, beyond merely selecting actions. It notes that ACGVF assumes full observability, while a prior approach used an internal belief state but required externally supplied goals. The note proposes to unify and extend these methods using hierarchical hidden Markov models (HHMMs).
arXiv:2606. 07017v1 Announce Type: new Abstract: Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap.
arXiv:2608. 02440v1 Announce Type: cross Abstract: In noisy social dilemmas, intended actions are stochastically corrupted before execution, so an observed defection may reflect hostile intent or action error.
arXiv:2606. 18820v1 Announce Type: cross Abstract: Sequential decision problems often exhibit an asymmetric evolution of information and decision flexibility: as a decision cycle unfolds, the agent receives richer information while feasible actions expire due to operational cutoffs, commitments, or resource constraints.
arXiv:2608. 10475v1 Announce Type: new Abstract: The emergence of language-based AI agents promises to transform the scope of machine economic activity.
arXiv:2608. 02509v1 Announce Type: cross Abstract: Sequential decision-making in real-world applications often involves uncertainty about the environment's model.
The paper investigates how a single pooled contract offered by an aggregator to heterogeneous smallholder farmers can be designed to maximize profit while addressing private adoption costs and unobserved effort over multiple seasons. Using a POMDP framework and reinforcement learning, the authors find that profit‑maximizing contracts disproportionately favor large farms, achieving 87.7% of possible adoption on large farms versus only 8.2% on smallholdings, largely due to higher measurement, reporting, and verification costs on smaller plots. The study suggests that adjusting MRV cost structures could reduce this disparity and help scale carbon farming to smallholders.
The paper introduces the Belief-State Engine (BSE), an inference module that supplies a large language model (LLM) with a Bayesian posterior over hidden states in a partially observable Markov decision process (POMDP). By keeping the raw action‑observation log hidden from the LLM, the BSE ensures the agent behaves as a sound Markov policy on the belief MDP, thereby inheriting classical POMDP optimality guarantees. Experiments on the Tiger POMDP and a red‑team attack‑graph task show that BSE‑augmented agents outperform six baselines in task return, belief calibration, and decision consistency.
Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible environments as a set of MDPs with shared states and actions but potentially different transition probabilities and rewards.
arXiv:2609. 20353v1 Announce Type: new Abstract: We study repeated contract design when a principal observes outcomes but not the actions that generate them.