Large Language Models (LLMs) are capable of transforming natural language domain descriptions into plausibly looking PDDL markup. However, ensuring that actions are consistent within domains still remains a challenging task. In this paper we present a novel concept to significantly improve the quality of LLM-generated PDDL models by performing automated consistency checking during the generation process. Although the proposed consistency checking strategies still can't guarantee absolute correctness of generated models, they can serve as valuable source of feedback reducing the amount of correction efforts expected from a human in the loop. We demonstrate the capabilities of our error detection approach on a number of classical and custom planning domains (logistics, gripper, tyreworld, household, pizza).
Generating Symbolic World Models via Test-time Scaling of Large Language Models
Westlake University（西湖大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Max Planck Institute for Intelligent Systems, Tübingen（智能系统马克斯·普朗克研究所（图宾根））；Environmental Systems Research Institute, Inc.（环境系统研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；SEED, Bytedance（字节跳动SEED团队）；The Hong Kong University of Science and Technology（香港科技大学）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔兰大学）；Center for European Research in Trusted Artificial Intelligence (CERTAIN)（欧洲可信人工智能研究中心）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
ICSI（国际计算机科学研究所）；UC Berkeley（加州大学伯克利分校）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image aspects which justify visual predictions. We propose a new model that focuses on the discriminating properties of the visible object, jointly predicts a class label, and explains why the predicted label is appropriate for the image. We propose a nov…
Colorado School of Mines（科罗拉多矿业学院）；US Army Research Laboratory（美国陆军研究实验室）
Terrain adaptation is an essential capability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected robot behaviors generated by terrain adaptation methods cannot always be executed accurately due to setbacks such as wheel slip and reduced tire pressure. To address this problem, we propose a novel approach for consistent behavior generation that enables the ground robot's actual behaviors to more accurately match expected behaviors while adapting to a variety of unstructured off-road terrain. Our approac…
