Learning task hierarchies using statistical semantics and goal reasoning

Gopalakrishnan, Sriram; Muñoz-Ávila, Héctor; Kuter, Ugur

doi:10.3233/aic-180756

Cited by 3 publications

(3 citation statements)

References 13 publications

(5 reference statements)

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“…For instance, in the chess game, the possible moves of a knight are naturally defined as follows: the knight moves two squares horizontally and then one square vertically, or it moves one square horizontally and then two squares vertically. More examples of procedural structures for action modeling are LTL preconditions represented as finite automata (Baier & McIlraith, 2006), sequence of effects executed in a particular order that may also include execution flow structures for branching and looping (Segovia-Aguas, , or hierarchies (Gopalakrishnan, Muñoz-Avila, & Kuter, 2018;Segovia-Aguas, Jiménez, & Jonsson, 2018).…”

Section: Applications and Open Challengesmentioning

confidence: 99%

A Comprehensive Framework for Learning Declarative Action Models

Aineto

Jiménez

Onaindía

2022

jair

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A declarative action model is a compact representation of the state transitions of dynamic systems that generalizes over world objects. The specification of declarative action models is often a complex hand-crafted task. In this paper we formulate declarative action models via state constraints, and present the learning of such models as a combinatorial search. The comprehensive framework presented here allows us to connect the learning of declarative action models to well-known problem solving tasks. In addition, our framework allows us to characterize the existing work in the literature according to four dimensions: (1) the target action models, in terms of the state transitions they define; (2) the available learning examples; (3) the functions used to guide the learning process, and to evaluate the quality of the learned action models; (4) the learning algorithm. Last, the paper lists relevant successful applications of the learning of declarative actions models and discusses some open challenges with the aim of encouraging future research work.

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Section: Applications and Open Challengesmentioning

confidence: 99%

A Comprehensive Framework for Learning Declarative Action Models

Aineto

Jiménez

Onaindía

2022

jair

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“…We can infer these preconditions by introducing a new artificial compound task for the method in question and compute its precondition. This seems especially useful when methods are learned in the first place (Lotinac and Jonsson 2016;Gopalakrishnan, Muñoz-Avila, and Kuter 2018;Xiao et al 2020).…”

Section: Preconditions Of Compound Tasksmentioning

confidence: 99%

Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks – A Complexity Analysis

Olz

Biundo

Bercher

2021

AAAI

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In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not specify preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: they are "hidden" in and depending on the decomposition structure. We formalize several kinds of preconditions and effects that can be inferred for compound tasks in totally ordered HTN domains. As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. We provide procedures for doing so, thereby extending previous work, which could only deal with acyclic models. We prove our procedures to be correct and complete for any totally ordered input domain. These results are embedded into an encompassing complexity analysis of the inference of preconditions and effects of compound tasks, an investigation that has not been made so far.

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“…Casting the learning problem of learning domain landmarks as word embeddings has been explored in previous work (Fine-Morris et al 2020;Gopalakrishnan, Muñoz-Avila, and Kuter 2018). They receive as input a collection of annotated plan traces.…”

Section: Related Workmentioning

confidence: 99%

HTN Learning via Transfer Learning of Domain Landmarks

Pennisi

Fine-Morris

Floyd

et al. 2021

FLAIRS

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Hierarchical Task Network (HTN) planning uses task-subtask relationships to break complex problems into more manageable subtasks, similar to how human problem-solvers plan. However, one limitation of HTN planning is that it requires domain knowledge in the form of planning methods to perform this task decomposition. Recent work has partially alleviated this knowledge engineering requirement by learning HTN methods from traces of observed behavior. Although this greatly reduces the amount of knowledge that must be encoded by a domain expert, it requires a large collection of traces in order to infer important landmark states that are used during trace segmentation and method learning. In this paper we present a novel method for landmark inference that transfers knowledge of landmarks from previously encountered environments to new environments without requiring any traces from the new environment. We evaluate our work in a logistics planning domain and show that our approach performs comparably to the existing landmark inference method but requires far fewer traces.

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Learning task hierarchies using statistical semantics and goal reasoning

Cited by 3 publications

References 13 publications

A Comprehensive Framework for Learning Declarative Action Models

A Comprehensive Framework for Learning Declarative Action Models

Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks – A Complexity Analysis

HTN Learning via Transfer Learning of Domain Landmarks

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