An Introduction to the Planning Domain Definition Language

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Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, methodology): “First-order representations structured in terms of objects and relations like PDDL [29, 21, 18], however, have a number of bene-ﬁts; in particular, they are easier to understand, and they can be easily 1 Universidad Sim´on Bol´ıvar, Venezuela.”
Cognitive Architectures for Language Agents (2023, background): “More sophisticated agents use if-else or while-if code structures (Wang et al., 2023a; Park et al., 2023); while agents deployed in well-defined domains may utilize structured simulators (Haslum et al., 2019) to generate plausible rollouts (Liu et al., 2023a; Dagan et al., 2023).”
Understanding the planning of LLM agents: A survey (2024, methodology): “Conventional works mainly rely on symbolic methods or reinforcement learning-based methods, such as Planning Domain Definition Language (PDDL) [Aeronautiques et al. , 1998; Haslum et al. , 2019] or policy learning [He et al. , 2015; Yao et al. , 2020a].”