A Structure for Plans and Behavior

By E. Sacerdoti. 1977

Subfields: LLM Agents & Tool Use, Knowledge Graphs & Representation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In LLM Agents & Tool Use

In Knowledge Graphs & Representation

Citations in the corpus

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022, background): “Task and motion planning [57, 58] is a problem of sequencing tasks to solve a high-level problem, while ensuring the feasibility given an embodiment (task planning [59, 60, 61]; motion planning [62]).”
Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Task and motion planning [22, 23] requires simultaneously solving a high-level, discrete task planning problem [24, 25, 26], and a low-level, continuous motion planning problem [27].”
Toward Vignette-Based Story Generation for Drama Management Systems (2007, background): “The eventual goal is to develop generative drama managers and experience managers that are more effective in adapting to the actions and intentions of interactive participants by generating better stories.”
Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments (2000, methodology): “For example, Steve and Adele model tasks using a hierarchical partial-order plan representation, which they generate automatically using task decomposition planning (Sacerdoti 1977).”
Planning Coherent Multisentential Text (1988, methodology): “The illustrate this contention, the Penman text structurer is a simplified top-down planner (as described first by [Sacerdoti 77]).”
Studies of Expertise from Psychological Perspectives (2006, background): “Such systems were progressively supplanted by ones in which the separation between knowledge and reasoning was not nearly as distinct, and in which general reasoning algorithms gave way to more narrowly applicable reasoning strategies, embedded in procedures for operating within specific domains of ”
TD Models: Modeling the World at a Mixture of Time Scales (1995, unknown): “…and abstract planning is afundamental problem in AI whether or not one usesthe reinforcement-learning framework (e.g., Fikes etal., 1972; Sacerdoti, 1977; Kuipers, 1979; Laird etal., 1986; Korf, 1985; Minton, 1988; Watkins, 1989;Drescher, 1991; Ring, 1991; Wixson, 1991; Schmidhu-ber,…”
A Digitalis Therapy Advisor with Explanations (1977, unknown): “(YES OR NO) n”
An Analysis of Forward Pruning (1994, methodology): “A more sophisticated version of this is quiescence search: mark a non-terminal 1In particular, we are developing a forward-pruning search technique for the game of bridge [17, 18], by extending task-network planning techniques [22, 23, 13, 20] to represent multi-agency and uncertainty.”
Compositional Planning Using Optimal Option Models (2012, background): “Classical planning algorithms make extensive use of temporal abstraction to construct high-level chunks of useful knowledge (Amarel, 1968; Sacerdoti, 1975; Korf, 1985; Laird et al., 1986).”