Zobrazeno 1 - 10
of 4 282
pro vyhledávání: '"PDDL"'
Autor:
Lequen, Arnaud
We consider the problem of synthesizing interpretable models that recognize the behaviour of an agent compared to other agents, on a whole set of similar planning tasks expressed in PDDL. Our approach consists in learning logical formulas, from a sma
Externí odkaz:
http://arxiv.org/abs/2410.10011
Although there is a growing demand for cooking behaviours as one of the expected tasks for robots, a series of cooking behaviours based on new recipe descriptions by robots in the real world has not yet been realised. In this study, we propose a robo
Externí odkaz:
http://arxiv.org/abs/2410.02874
Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks. Nevertheless, it remains a significant challenge to handle long-hori
Externí odkaz:
http://arxiv.org/abs/2409.20560
Autor:
Liu, Ruikai1,2,3,4 (AUTHOR) liuruikai@sia.cn, Wan, Guangxi1,2,3 (AUTHOR) wanguangxi@sia.cn, Jiang, Maowei5 (AUTHOR), Chen, Haojie1,2,3,4 (AUTHOR), Zeng, Peng1,2,3 (AUTHOR) wanguangxi@sia.cn
Publikováno v:
Biomimetics (2313-7673). Oct2024, Vol. 9 Issue 10, p612. 17p.
Publikováno v:
2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)
Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contrib
Externí odkaz:
http://arxiv.org/abs/2408.08145
Large Language Models (LLMs) have shown remarkable performance in various natural language tasks, but they often struggle with planning problems that require structured reasoning. To address this limitation, the conversion of planning problems into t
Externí odkaz:
http://arxiv.org/abs/2407.12979
Autor:
Chen, Qiguang, Pan, Ya-Jun
Industry 4.0 proposes the integration of artificial intelligence (AI) into manufacturing and other industries to create smart collaborative systems which enhance efficiency. The aim of this paper is to develop a flexible and adaptive framework to gen
Externí odkaz:
http://arxiv.org/abs/2407.08534
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
Externí odkaz:
http://arxiv.org/abs/2404.07751
Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designing such an a
Externí odkaz:
http://arxiv.org/abs/2403.15251
LLMs are being increasingly used for planning-style tasks, but their capabilities for planning and reasoning are poorly understood. We present AutoPlanBench, a novel method for automatically converting planning benchmarks written in PDDL into textual
Externí odkaz:
http://arxiv.org/abs/2311.09830