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1. Dynamic Awareness}

   https://doi.org/10.24963/kr.2020/48  

   Halpern, Joseph Y.; Piermont, Evan (September 2020, Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2020))

   We investigate how to model the beliefs of an agent who becomes more aware. We use the framework of Halpern and Rego (2013) by adding probability, and define a notion of a model transition that describes constraints on how, if an agent becomes aware of a new formula φ in state s of a model M, she transitions to state s* in a model M*. We then discuss how such a model can be applied to information disclosure. 

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2. A Cost-Aware Multi-Agent System for Black-Box Design Space Exploration

   https://doi.org/10.1115/1.4065914  

   Chen, Siyu; Bayrak, Alparslan Emrah; Sha, Zhenghui (August 2024, Journal of Mechanical Design)

   Effective coordination of design teams must account for the influence of costs incurred while searching for the best design solutions. This article introduces a cost-aware multi-agent system (MAS), a theoretical model to (1) explain how individuals in a team should search, assuming that they are all rational utility-maximizing decision-makers and (2) study the impact of cost on the search performance of both individual agents and the system. First, we develop a new multi-agent Bayesian optimization framework accounting for information exchange among agents to support their decisions on where to sample in search. Second, we employ a reinforcement learning approach based on the multi-agent deep deterministic policy gradient for training MAS to identify where agents cannot sample due to design constraints. Third, we propose a new cost-aware stopping criterion for each agent to determine when costs outweigh potential gains in search as a criterion to stop. Our results indicate that cost has a more significant impact on MAS communication in complex design problems than in simple ones. For example, when searching in complex design spaces, some agents could initially have low-performance gains, thus stopping prematurely due to negative payoffs, even if those agents could perform better in the later stage of the search. Therefore, global-local communication becomes more critical in such situations for the entire system to converge. The proposed model can serve as a benchmark for empirical studies to quantitatively gauge how humans would rationally make design decisions in a team. 

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3. Dynamic size counting in population protocols

   David Doty and Mahsa Eftekhari (January 2022, SAND 2022: 1st Symposium on Algorithmic Foundations of Dynamic Networks)

   James Aspnes and Othon Michail (Ed.)

   The population protocol model describes a network of anonymous agents that interact asynchronously in pairs chosen at random. Each agent starts in the same initial state s. We introduce the *dynamic size counting* problem: approximately counting the number of agents in the presence of an adversary who at any time can remove any number of agents or add any number of new agents in state s. A valid solution requires that after each addition/removal event, resulting in population size n, with high probability each agent "quickly" computes the same constant-factor estimate of the value log2n (how quickly is called the *convergence* time), which remains the output of every agent for as long as possible (the *holding* time). Since the adversary can remove agents, the holding time is necessarily finite: even after the adversary stops altering the population, it is impossible to *stabilize* to an output that never again changes. We first show that a protocol solves the dynamic size counting problem if and only if it solves the *loosely-stabilizing counting* problem: that of estimating logn in a *fixed-size* population, but where the adversary can initialize each agent in an arbitrary state, with the same convergence time and holding time. We then show a protocol solving the loosely-stabilizing counting problem with the following guarantees: if the population size is n, M is the largest initial estimate of logn, and s is the maximum integer initially stored in any field of the agents' memory, we have expected convergence time O(logn+logM), expected polynomial holding time, and expected memory usage of O(log2(s)+(loglogn)2) bits. Interpreted as a dynamic size counting protocol, when changing from population size nprev to nnext, the convergence time is O(lognnext+loglognprev). 

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4. PACE: A Framework for Learning and Control in Linear Incomplete-Information Differential Games

   Soltanian, Seyed Yousef; Zhang, Wenlong (May 2025, Proceedings of Machine Learning Research)

   In this paper, we address the problem of a two-player linear quadratic differential game with incomplete information, a scenario commonly encountered in multi-agent control, human-robot interaction (HRI), and approximation methods to solve general-sum differential games. While solutions to such linear differential games are typically obtained through coupled Riccati equations, the complexity increases when agents have incomplete information, particularly when neither is aware of the other’s cost function. To tackle this challenge, we propose a model-based Peer-Aware Cost Estimation (PACE) framework for learning the cost parameters of the other agent. In PACE, each agent treats its peer as a learning agent rather than a stationary optimal agent, models their learning dynamics, and leverages this dynamic to infer the cost function parameters of the other agent. This approach enables agents to infer each other’s objective function in real time based solely on their previous state observations and dynamically adapt their control policies. Furthermore, we provide a theoretical guarantee for the convergence of parameter estimation and the stability of system states in PACE. Additionally, using numerical studies, we demonstrate how modeling the learning dynamics of the other agent benefits PACE, compared to approaches that approximate the other agent as having complete information, particularly in terms of stability and convergence speed. 

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5. Social ODE: Multi-agent Trajectory Forecasting with Neural Ordinary Differential Equations.

   https://doi.org/10.1007/978-3-031-20047-2_13  

   Wen, S.; Wang, H.; Metaxas, D. (January 2022, Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science)

   Avidan, S.; Brostow, G.; Cissé, M.; Farinella, G.M.; Hassner, T. (Ed.)

   Wen, S., Wang, H., Metaxas, D. (2022). Social ODE: Multi-agent Trajectory Forecasting with Neural Ordinary Differential Equations. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_13 Multi-agent trajectory forecasting has recently attracted a lot of attention due to its widespread applications including autonomous driving. Most previous methods use RNNs or Transformers to model agent dynamics in the temporal dimension and social pooling or GNNs to model interactions with other agents; these approaches usually fail to learn the underlying continuous temporal dynamics and agent interactions explicitly. To address these problems, we propose Social ODE which explicitly models temporal agent dynamics and agent interactions. Our approach leverages Neural ODEs to model continuous temporal dynamics, and incorporates distance, interaction intensity, and aggressiveness estimation into agent interaction modeling in latent space. We show in extensive experiments that our Social ODE approach compares favorably with state-of-the-art, and more importantly, can successfully avoid sudden obstacles and effectively control the motion of the agent, while previous methods often fail in such cases. 

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