10 research outputs found
Learning STRIPS Action Models with Classical Planning
This paper presents a novel approach for learning STRIPS action models from
examples that compiles this inductive learning task into a classical planning
task. Interestingly, the compilation approach is flexible to different amounts
of available input knowledge; the learning examples can range from a set of
plans (with their corresponding initial and final states) to just a pair of
initial and final states (no intermediate action or state is given). Moreover,
the compilation accepts partially specified action models and it can be used to
validate whether the observation of a plan execution follows a given STRIPS
action model, even if this model is not fully specified.Comment: 8+1 pages, 4 figures, 6 table
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
We achieved a new milestone in the difficult task of enabling agents to learn
about their environment autonomously. Our neuro-symbolic architecture is
trained end-to-end to produce a succinct and effective discrete state
transition model from images alone. Our target representation (the Planning
Domain Definition Language) is already in a form that off-the-shelf solvers can
consume, and opens the door to the rich array of modern heuristic search
capabilities. We demonstrate how the sophisticated innate prior we place on the
learning process significantly reduces the complexity of the learned
representation, and reveals a connection to the graph-theoretic notion of
"cube-like graphs", thus opening the door to a deeper understanding of the
ideal properties for learned symbolic representations. We show that the
powerful domain-independent heuristics allow our system to solve visual
15-Puzzle instances which are beyond the reach of blind search, without
resorting to the Reinforcement Learning approach that requires a huge amount of
training on the domain-dependent reward information.Comment: Accepted in IJCAI 2020 main track (accept ratio 12.6%). The prequel
of this paper, "The Search for STRIPS", can be found here: arXiv:1912.05492 .
(update, 2020/08/11) We expanded the related work sectio
Learning Symbolic Operators for Task and Motion Planning
Robotic planning problems in hybrid state and action spaces can be solved by
integrated task and motion planners (TAMP) that handle the complex interaction
between motion-level decisions and task-level plan feasibility. TAMP approaches
rely on domain-specific symbolic operators to guide the task-level search,
making planning efficient. In this work, we formalize and study the problem of
operator learning for TAMP. Central to this study is the view that operators
define a lossy abstraction of the transition model of a domain. We then propose
a bottom-up relational learning method for operator learning and show how the
learned operators can be used for planning in a TAMP system. Experimentally, we
provide results in three domains, including long-horizon robotic planning
tasks. We find our approach to substantially outperform several baselines,
including three graph neural network-based model-free approaches from the
recent literature. Video: https://youtu.be/iVfpX9BpBRo Code:
https://git.io/JCT0gComment: IROS 202
STRIPS Action Discovery
The problem of specifying high-level knowledge bases for planning becomes a
hard task in realistic environments. This knowledge is usually handcrafted and
is hard to keep updated, even for system experts. Recent approaches have shown
the success of classical planning at synthesizing action models even when all
intermediate states are missing. These approaches can synthesize action schemas
in Planning Domain Definition Language (PDDL) from a set of execution traces
each consisting, at least, of an initial and final state. In this paper, we
propose a new algorithm to unsupervisedly synthesize STRIPS action models with
a classical planner when action signatures are unknown. In addition, we
contribute with a compilation to classical planning that mitigates the problem
of learning static predicates in the action model preconditions, exploits the
capabilities of SAT planners with parallel encodings to compute action schemas
and validate all instances. Our system is flexible in that it supports the
inclusion of partial input information that may speed up the search. We show
through several experiments how learned action models generalize over unseen
planning instances.Comment: Presented to Genplan 2020 workshop, held in the AAAI 2020 conference
(https://sites.google.com/view/genplan20) (2021/03/05: included missing
acknowledgments
A common framework for learning causality
[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government.Onaindia De La Rivaherrera, E.; Aineto, D.; Jiménez-Celorrio, S. (2018). A common framework for learning causality. Progress in Artificial Intelligence. 7(4):351-357. https://doi.org/10.1007/s13748-018-0151-yS35135774Aineto, D., Jiménez, S., Onaindia, E.: Learning STRIPS action models with classical planning. In: International Conference on Automated Planning and Scheduling, ICAPS-18 (2018)Amir, E., Chang, A.: Learning partially observable deterministic action models. J. Artif. Intell. Res. 33, 349–402 (2008)Asai, M., Fukunaga, A.: Classical planning in deep latent space: bridging the subsymbolic–symbolic boundary. In: National Conference on Artificial Intelligence, AAAI-18 (2018)Cresswell, S.N., McCluskey, T.L., West, M.M.: Acquiring planning domain models using LOCM. Knowl. Eng. Rev. 28(02), 195–213 (2013)Ebert-Uphoff, I.: Two applications of causal discovery in climate science. In: Workshop Case Studies of Causal Discovery with Model Search (2013)Ebert-Uphoff, I., Deng, Y.: Causal discovery from spatio-temporal data with applications to climate science. In: 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, MI, USA, 3–6 December 2014, pp. 606–613 (2014)Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic causal theories. Artif. Intell. 153(1–2), 49–104 (2004)Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach. Part I: Causes. Br. J. Philos. Sci. 56(4), 843–887 (2005)Heckerman, D., Meek, C., Cooper, G.: A Bayesian approach to causal discovery. In: Jain, L.C., Holmes, D.E. (eds.) Innovations in Machine Learning. Theory and Applications, Studies in Fuzziness and Soft Computing, chapter 1, pp. 1–28. Springer, Berlin (2006)Li, J., Le, T.D., Liu, L., Liu, J., Jin, Z., Sun, B.-Y., Ma, S.: From observational studies to causal rule mining. ACM TIST 7(2), 14:1–14:27 (2016)Malinsky, D., Danks, D.: Causal discovery algorithms: a practical guide. Philos. Compass 13, e12470 (2018)McCain, N., Turner, H.: Causal theories of action and change. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, AAAI 97, IAAI 97, 27–31 July 1997, Providence, Rhode Island, pp. 460–465 (1997)McCarthy, J.: Epistemological problems of artificial intelligence. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, USA, 22–25 August 1977, pp. 1038–1044 (1977)McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. Mach. Intell. 4, 463–502 (1969)Pearl, J.: Reasoning with cause and effect. AI Mag. 23(1), 95–112 (2002)Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press, Cambridge (2009)Spirtes, C.G.P., Scheines, R.: Causation, Prediction and Search, 2nd edn. The MIT Press, Cambridge (2001)Spirtes, P., Zhang, K.: Causal discovery and inference: concepts and recent methodological advances. Appl. Inform. 3, 3 (2016)Thielscher, M.: Ramification and causality. Artif. Intell. 89(1–2), 317–364 (1997)Triantafillou, S., Tsamardinos, I.: Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Mach. Learn. Res. 16, 2147–2205 (2015)Yang, Q., Kangheng, W., Jiang, Y.: Learning action models from plan examples using weighted MAX-SAT. Artif. Intell. 171(2–3), 107–143 (2007)Zhuo, H.H., Kambhampati, S: Action-model acquisition from noisy plan traces. In: International Joint Conference on Artificial Intelligence, IJCAI-13, pp. 2444–2450. AAAI Press (2013
Classical Planning in Deep Latent Space
Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose Latplan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), Latplan learns a complete propositional PDDL
action model of the environment. Later, when a pair of images representing the
initial and the goal states (planning inputs) is given, Latplan finds a plan to
the goal state in a symbolic latent space and returns a visualized plan
execution. We evaluate Latplan using image-based versions of 6 planning
domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of
LightsOut.Comment: Under review at Journal of Artificial Intelligence Research (JAIR
A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans
[EN] Learning in AI planning tries to recognize past conducts to predict features that help improve action models.
We propose a constraint programming approach for learning the temporal features, i.e., the distribution of conditions/effects and durations, of actions in an expressive temporal planning model with overlapping actions, which makes it suitable for knowledge-based multi-agent systems.
We automatically build a purely declarative formulation that models time-stamps for durative actions, causal link relationships, threats and effect interferences from an arbitrary number of input plans: from just a unique single trace to many.
We accommodate different degrees of input knowledge and support a different range of expressiveness, subsuming the PDDL2.1 temporal semantics. The formulation is simple but effective, and is not only valid for learning, but also for plan validation, as shown in its evaluation that returns high precision and accuracy values.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Garrido, A. (2022). A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans. Constraints. 27(1-2):134-160. https://doi.org/10.1007/s10601-022-09330-3134160271-2Geffner, H., & Bonet, B. (2013). A concise introduction to models and methods for automated planning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 8(1), 1–141.Ghallab, M., Nau, D., & Traverso, P. (2004). Automated Planning: Theory and Practice. Elsevier.Fox, M., & Long, D. (2003). PDDL2.1: An extension to PDDL for expressing temporal planning domains. 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