STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving

By R. Fikes, Nils J. Nilsson. 1971

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In Reinforcement Learning

Citations in the corpus

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022, background): “Classically, this problem has been solved through symbolic planning [61, 63] or optimization [65, 66], but these require explicit primitives and constraints.”
Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “5 to better handle the above long-horizon tasks with a high-level STRIPS planner using a parameterized set of skills: Pick, Place, Open fridge door, Close fridge door, Open drawer, Close drawer, and Navigate.”
Relational inductive biases, deep learning, and graph networks (2018, background): “ and was central to many structured approaches, including logic, grammars, classic planning, graphical models, causal reasoning, Bayesian nonparametrics, and probabilistic programming (Chomsky, 1957; Nilsson and Fikes, 1970; Pearl, 1986, 2009; Russell and Norvig, 2009; Hjort et al., 2010; Goodman et”
Improving Robot Controller Transparency Through Autonomous Policy Explanation (2017, methodology): “We use a Boolean algebra over planning predicates [11] as a basis for grounding state regions in natural language (Section 5).”
IQA: Visual Question Answering in Interactive Environments (2017, background): “A large body of research on planning algorithms [12, 14, 26, 62, 63] use high-level formal languages.”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “In the classical planning paradigm (strips; Fikes & Nilsson, 1971), actions are represented as tuples of such preconditions and effects.”
Visual Semantic Planning Using Deep Successor Representations (2017, methodology): “The STRIPS formulation enables us to define the rules of the scene, such as object affordances and causality of actions.”
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains (2003, background): “Classical linear planners (Fikes & Nilsson, 1971; Russell & Norvig, 1995) rely on the simple fact that a total ordering on the points at which actions are applied can be trivially embedded into a time line.”
Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration (2018, background): “Conventionally task planning focuses on high-level plans and low-level state spaces [10, 36].”
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning (2018, methodology): “For clarity of exposition, we formalize STRIPS (Fikes and Nilsson 1971) PDDL problems; however, our approach also applies to Action Description Language (ADL) (Pednault 1989) features such as typing, disjunctions, negative preconditions, existential quantifiers, finite universal quantifiers, conditi”
On the undecidability of probabilistic planning and related stochastic optimization problems (2003, methodology): “This model is based on STRIPS propositional planning [18] with uncertainty in the form of (conditional) probability distributions added to the action effects.”
Nonmonotonic causal theories (2004, background): “Several other approaches to describing effects of actions have been proposed later, including the situation calculus [26], the STRIPS language [9] and the event calculus [16].”
Predictive Robot Programming: Theoretical and Experimental Analysis (2004, methodology): “A search, using a pre-and post-condition theorem-proving model (Fikes and Nilsson 1971), determines a sequence of primitives that describes the actions of the user.”
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage (1999, methodology): “For instance, in the Strips action representation (Fikes & Nilsson,1971), the state transitions induced by actions are represented implicitly by describing thee ects of actions on only those features that change value when the action is executed.”
Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, unknown): “The comparative summary of the embodied robots, simulators, datasets, and approaches provides a clear picture of the recent development in embodied AI, which greatly beneﬁts the future research along this emerging and promising research direction.”