LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 272

Search options

  1. Book ; Online: Learning to Play Trajectory Games Against Opponents with Unknown Objectives

    Liu, Xinjie / Peters, Lasse / Alonso-Mora, Javier

    2022  

    Abstract: Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such ... ...

    Abstract Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are known. To make such tools applicable for ego-centric planning with only local information, we propose an adaptive model-predictive game solver, which jointly infers other players' objectives online and computes a corresponding generalized Nash equilibrium (GNE) strategy. The adaptivity of our approach is enabled by a differentiable trajectory game solver whose gradient signal is used for maximum likelihood estimation (MLE) of opponents' objectives. This differentiability of our pipeline facilitates direct integration with other differentiable elements, such as neural networks (NNs). Furthermore, in contrast to existing solvers for cost inference in games, our method handles not only partial state observations but also general inequality constraints. In two simulated traffic scenarios, we find superior performance of our approach over both existing game-theoretic methods and non-game-theoretic model-predictive control (MPC) approaches. We also demonstrate our approach's real-time planning capabilities and robustness in two hardware experiments.
    Keywords Computer Science - Robotics
    Subject code 629
    Publishing date 2022-11-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Book ; Online: iLQGames.jl

    Peters, Lasse / Sunberg, Zachary N.

    Rapidly Designing and Solving Differential Games in Julia

    2020  

    Abstract: In many problems that involve multiple decision making agents, optimal choices for each agent depend on the choices of others. Differential game theory provides a principled formalism for expressing these coupled interactions and recent work offers ... ...

    Abstract In many problems that involve multiple decision making agents, optimal choices for each agent depend on the choices of others. Differential game theory provides a principled formalism for expressing these coupled interactions and recent work offers efficient approximations to solve these problems to non-cooperative equilibria. iLQGames.jl is a framework for designing and solving differential games, built around the iterative linear-quadratic method. It is written in the Julia programming language to allow flexible prototyping and integration with other research software, while leveraging the high-performance nature of the language to allow real-time execution. The open-source software package can be found at https://github.com/lassepe/iLQGames.jl.
    Keywords Computer Science - Multiagent Systems
    Publishing date 2020-02-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories

    Li, Jingqi / Chiu, Chih-Yuan / Peters, Lasse / Sojoudi, Somayeh / Tomlin, Claire / Fridovich-Keil, David

    2023  

    Abstract: In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and trajectory of each agent may be unavailable to the other agents. Prior ... ...

    Abstract In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and trajectory of each agent may be unavailable to the other agents. Prior work on using partial observations to infer the costs in dynamic games assumes an open-loop information pattern. In this work, we demonstrate that the feedback Nash equilibrium concept is more expressive and encodes more complex behavior. It is desirable to develop specific tools for inferring players' objectives in feedback games. Therefore, we consider the dynamic game cost inference problem under the feedback information pattern, using only partial state observations and incomplete trajectory data. To this end, we first propose an inverse feedback game loss function, whose minimizer yields a feedback Nash equilibrium state trajectory closest to the observation data. We characterize the landscape and differentiability of the loss function. Given the difficulty of obtaining the exact gradient, our main contribution is an efficient gradient approximator, which enables a novel inverse feedback game solver that minimizes the loss using first-order optimization. In thorough empirical evaluations, we demonstrate that our algorithm converges reliably and has better robustness and generalization performance than the open-loop baseline method when the observation data reflects a group of players acting in a feedback Nash game.

    Comment: Accepted by AAMAS 2023. This is a preprint version
    Keywords Computer Science - Multiagent Systems ; Computer Science - Robotics ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 629
    Publishing date 2023-01-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: Contingency Games for Multi-Agent Interaction

    Peters, Lasse / Bajcsy, Andrea / Chiu, Chih-Yuan / Fridovich-Keil, David / Laine, Forrest / Ferranti, Laura / Alonso-Mora, Javier

    2023  

    Abstract: Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on ... ...

    Abstract Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot's actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.
    Keywords Computer Science - Robotics
    Subject code 629
    Publishing date 2023-04-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: Learning Mixed Strategies in Trajectory Games

    Peters, Lasse / Fridovich-Keil, David / Ferranti, Laura / Stachniss, Cyrill / Alonso-Mora, Javier / Laine, Forrest

    2022  

    Abstract: In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this ... ...

    Abstract In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional "predict then plan" approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive "mixed" strategies. We validate our approach on a number of experiments using the pursuit-evasion game "tag."
    Keywords Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 004
    Publishing date 2022-04-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Book ; Online: Online and Offline Learning of Player Objectives from Partial Observations in Dynamic Games

    Peters, Lasse / Rubies-Royo, Vicenç / Tomlin, Claire J. / Ferranti, Laura / Alonso-Mora, Javier / Stachniss, Cyrill / Fridovich-Keil, David

    2023  

    Abstract: Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve ... ...

    Abstract Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.

    Comment: arXiv admin note: text overlap with arXiv:2106.03611
    Keywords Computer Science - Robotics
    Subject code 006 ; 629
    Publishing date 2023-02-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Book ; Online: Inference-Based Strategy Alignment for General-Sum Differential Games

    Peters, Lasse / Fridovich-Keil, David / Tomlin, Claire J. / Sunberg, Zachary N.

    2020  

    Abstract: In many settings where multiple agents interact, the optimal choices for each agent depend heavily on the choices of the others. These coupled interactions are well-described by a general-sum differential game, in which players have differing objectives, ...

    Abstract In many settings where multiple agents interact, the optimal choices for each agent depend heavily on the choices of the others. These coupled interactions are well-described by a general-sum differential game, in which players have differing objectives, the state evolves in continuous time, and optimal play may be characterized by one of many equilibrium concepts, e.g., a Nash equilibrium. Often, problems admit multiple equilibria. From the perspective of a single agent in such a game, this multiplicity of solutions can introduce uncertainty about how other agents will behave. This paper proposes a general framework for resolving ambiguity between equilibria by reasoning about the equilibrium other agents are aiming for. We demonstrate this framework in simulations of a multi-player human-robot navigation problem that yields two main conclusions: First, by inferring which equilibrium humans are operating at, the robot is able to predict trajectories more accurately, and second, by discovering and aligning itself to this equilibrium the robot is able to reduce the cost for all players.
    Keywords Computer Science - Robotics ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 629
    Publishing date 2020-02-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Book ; Online: Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations

    Peters, Lasse / Fridovich-Keil, David / Rubies-Royo, Vicenç / Tomlin, Claire J. / Stachniss, Cyrill

    2021  

    Abstract: Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theoretical framework for modeling scenarios involving multiple agents with ... ...

    Abstract Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theoretical framework for modeling scenarios involving multiple agents with differing objectives interacting over time. A core challenge when formulating a dynamic game is designing objectives for each agent that capture desired behavior. In this paper, we propose a method for inferring parametric objective models of multiple agents based on observed interactions. Our inverse game solver jointly optimizes player objectives and continuous-state estimates by coupling them through Nash equilibrium constraints. Hence, our method is able to directly maximize the observation likelihood rather than other non-probabilistic surrogate criteria. Our method does not require full observations of game states or player strategies to identify player objectives. Instead, it robustly recovers this information from noisy, partial state observations. As a byproduct of estimating player objectives, our method computes a Nash equilibrium trajectory corresponding to those objectives. Thus, it is suitable for downstream trajectory forecasting tasks. We demonstrate our method in several simulated traffic scenarios. Results show that it reliably estimates player objectives from a short sequence of noise-corrupted partial state observations. Furthermore, using the estimated objectives, our method makes accurate predictions of each player's trajectory.

    Comment: Submitted to RSS2021
    Keywords Computer Science - Robotics ; Computer Science - Multiagent Systems
    Subject code 629
    Publishing date 2021-06-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  9. Book ; Online: Scenario-Game ADMM

    Li, Jingqi / Chiu, Chih-Yuan / Peters, Lasse / Palafox, Fernando / Karabag, Mustafa / Alonso-Mora, Javier / Sojoudi, Somayeh / Tomlin, Claire / Fridovich-Keil, David

    A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games

    2023  

    Abstract: Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochastic, general-sum, pure Nash games, where each player has an expected-value ... ...

    Abstract Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochastic, general-sum, pure Nash games, where each player has an expected-value objective and a set of chance constraints. This new approximation scheme inherits the accuracy of objective approximation from the established sample average approximation (SAA) method and enjoys a feasibility guarantee derived from the scenario optimization literature. We characterize the sample complexity of this new game-theoretic approximation scheme, and observe that high accuracy usually requires a large number of samples, which results in a large number of sampled constraints. To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game. We prove the convergence of our algorithm to a GNE and empirically demonstrate superior performance relative to a recent baseline algorithm based on ADMM and interior point method.
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 518
    Publishing date 2023-04-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  10. Article ; Online: Commentaries on Viewpoint: Consider iron status when making sex comparisons in human physiology.

    Badenhorst, Claire E / Millet, Grégoire P / Debevec, Tadej / Brocherie, Franck / Coates, Alexandra M / Burr, Jamie F / Andersen, Andreas Breenfeldt / Bejder, Jacob / Gliemann, Lasse / Pedlar, Charles R / Brugnara, Carlo / Shiffman, Viviana J / Peters, Carli M / Sheel, Andrew W / Bruinvels, Georgie / Govus, Andrew D / Shivgulam, Madeline E / Petterson, Jennifer L / O’Brien, Myles W /
    Burden, R J / Besson, Thibault / Ansdell, Paul / Sánchez-Briones, Maria E

    Journal of applied physiology (Bethesda, Md. : 1985)

    2022  Volume 132, Issue 3, Page(s) 703–709

    MeSH term(s) Humans ; Iron
    Chemical Substances Iron (E1UOL152H7)
    Language English
    Publishing date 2022-03-11
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 219139-8
    ISSN 1522-1601 ; 0021-8987 ; 0161-7567 ; 8750-7587
    ISSN (online) 1522-1601
    ISSN 0021-8987 ; 0161-7567 ; 8750-7587
    DOI 10.1152/japplphysiol.00016.2022
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Uc I Zs.249: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top