Cyber Physical Games

• URL: http://arxiv.org/abs/2407.05817v1
• Date: Mon, 8 Jul 2024 10:54:14 GMT
• Title: Cyber Physical Games
• Authors: Warisa Sritriratanarak, Paulo Garcia,
• Abstract summary: We show that the non-determinism inherent in the communication medium between agents and the underlying physical environment gives rise to environment evolution. We name these emergent properties Cyber Physical Games and study its properties. We present an algorithmic model that determines the most likely system evolution, approximating Cyber Physical Games through Probabilistic Finite State Automata.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe a formulation of multi-agents operating within a Cyber-Physical System, resulting in collaborative or adversarial games. We show that the non-determinism inherent in the communication medium between agents and the underlying physical environment gives rise to environment evolution that is a probabilistic function of agents' strategies. We name these emergent properties Cyber Physical Games and study its properties. We present an algorithmic model that determines the most likely system evolution, approximating Cyber Physical Games through Probabilistic Finite State Automata, and evaluate it on collaborative and adversarial versions of the Iterated Boolean Game, comparing theoretical results with simulated ones. Results support the validity of the proposed model, and suggest several required research directions to continue evolving our understanding of Cyber Physical System, as well as how to best design agents that must operate within such environments.

Related papers

• Expectation vs. Reality: Towards Verification of Psychological Games [18.30789345402813]
  Psychological games (PGs) were developed as a way to model and analyse agents with belief-dependent motivations. This paper proposes methods to solve PGs and implementing them within PRISM-games, a formal verification tool for games.
  arXiv  Detail & Related papers  (2024-11-08T14:41:52Z)
• Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning [2.992602379681373]
  We introduce an episodic future thinking (EFT) mechanism for a reinforcement learning (RL) agent. We first develop a multi-character policy that captures diverse characters with an ensemble of heterogeneous policies. Once the character is inferred, the agent predicts the upcoming actions of target agents and simulates the potential future scenario.
  arXiv  Detail & Related papers  (2024-10-22T19:12:42Z)
• ContPhy: Continuum Physical Concept Learning and Reasoning from Videos [86.63174804149216]
  ContPhy is a novel benchmark for assessing machine physical commonsense. We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy. We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models.
  arXiv  Detail & Related papers  (2024-02-09T01:09:21Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• Energy-based Potential Games for Joint Motion Forecasting and Control [0.125828876338076]
  This work uses game theory as a mathematical framework to address interaction modeling in motion forecasting and control. We establish a connection between differential games, optimal control, and energy-based models, demonstrating how existing approaches can be unified under our proposed Energy-based Potential Game formulation. We introduce a new end-to-end learning application that combines neural networks for game- parameter inference with a differentiable game-theoretic optimization layer, acting as an inductive bias.
  arXiv  Detail & Related papers  (2023-12-04T11:30:26Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions [0.13499500088995461]
  We show a connection between differential games, optimal control, and energy-based models. Building upon this formulation, this work introduces a new end-to-end learning application. Experiments using simulated mobile robot pedestrian interactions and real-world automated driving data provide empirical evidence.
  arXiv  Detail & Related papers  (2023-08-31T08:30:11Z)
• Modelling Behaviour Change using Cognitive Agent Simulations [0.0]
  This paper presents work-in-progress research to apply selected behaviour change theories to simulated agents. The research is focusing on complex agent architectures required for self-determined goal achievement in adverse circumstances.
  arXiv  Detail & Related papers  (2021-10-16T19:19:08Z)
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning [68.74447489372037]
  We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning. A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
  arXiv  Detail & Related papers  (2021-06-25T00:21:41Z)
• ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation [75.0278287071591]
  ThreeDWorld (TDW) is a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science.
  arXiv  Detail & Related papers  (2020-07-09T17:33:27Z)
• RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
  We introduce RoboTHOR to democratize research in interactive and embodied visual AI. We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
  arXiv  Detail & Related papers  (2020-04-14T20:52:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.