Automated Domain Model Learning Tools for Planning

Jilani, Rabia

doi:10.1007/978-3-030-38561-3_2

Cited by 3 publications

(6 citation statements)

References 37 publications

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“…For example, in the Blocksworld domain, ‘only one block can be on top of some other block’ or ‘a block being held means that the robotic arm is not empty’ is an illustration of BK. To compensate for the absence of domain knowledge, this BK may also comprise of object type definitions as well as predicate and intermediate state information (Jilani et al ., 2014).…”

Section: Characteristics Of Learning Systemsmentioning

confidence: 99%

“…LOCM (Cresswell et al ., 2009, 2013) uses an object-centered representation and learns a set of parameterized Finite State Machines (FSM), where each FSM represents pre and post-conditions of the domain operators (Jilani et al ., 2014). A planning domain consists of sets (called sorts) of object instances, where each object behaves in the same way as any other object in its sort.…”

Section: Type Of Learningmentioning

confidence: 99%

“…For each action in the training solution sequence, it asks the user to input, if needed, the target state that each object would occupy after the action execution, thus creating its operator schema step-by-step. It is implemented inside a graphic tool called Graphical Interface for Planning with Objects, which facilitates user interaction, domain modeling and operator induction (McCluskey et al ., 2002; Jilani et al ., 2014).…”

Section: Type Of Learningmentioning

confidence: 99%

“…This reward function output is used to update the weights of the candidate models. The model with the highest weight is rendered as the model being searched for (Jilani et al ., 2014).…”

Section: Type Of Learningmentioning

confidence: 99%

“…KE for AP is the process that deals with the acquisition, formulation, validation and maintenance of planning knowledge, where the essential output is the domain model (Jilani et al ., 2014). The main issue of current KE approaches for encoding domain models is that they require specific expertise which can only be furnished by a domain expert.…”

Section: Introductionmentioning

confidence: 99%

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A review of learning planning action models

Arora¹,

Fiorino²,

Pellier³

et al. 2018

The Knowledge Engineering Review

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For social robots to be brought more into widespread use in the fields of companionship, care taking and domestic help, they must be capable of demonstrating social intelligence. In order to be acceptable, they must exhibit socio-communicative skills. Classic approaches to program HRI from observed human-human interactions fails to capture the subtlety of multimodal interactions as well as the key structural differences between robots and humans. The former arises due to a difficulty in quantifying and coding multimodal behaviours, while the latter due to a difference of the degrees of liberty between a robot and a human. However, the notion of reverse engineering from multimodal HRI traces to learn the underlying behavioral blueprint of the robot given multimodal traces seems an option worth exploring. With this spirit, the entire HRI can be seen as a sequence of exchanges of speech acts between the robot and human, each act treated as an action, bearing in mind that the entire sequence is goal-driven. Thus, this entire interaction can be treated as a sequence of actions propelling the interaction from its initial to goal state, also known as a plan in the domain of AI planning. In the same domain, this action sequence that stems from plan execution can be represented as a trace. AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal traces. This article reviews recent machine learning techniques for learning planning action models which can be applied to the field of HRI with the intent of rendering robots as socio-communicative.

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Section: Characteristics Of Learning Systemsmentioning

confidence: 99%

Section: Type Of Learningmentioning

confidence: 99%

Section: Type Of Learningmentioning

confidence: 99%

“…This reward function output is used to update the weights of the candidate models. The model with the highest weight is rendered as the model being searched for (Jilani et al ., 2014).…”

Section: Type Of Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A review of learning planning action models

Arora¹,

Fiorino²,

Pellier³

et al. 2018

The Knowledge Engineering Review

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Reformulation techniques for automated planning: a systematic review

Alarnaouti,

Baryannis,

Vallati

2023

The Knowledge Engineering Review

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Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.

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Learning a Planning Domain Model From Natural Language Process Manuals

et al. 2020

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Artificial intelligence planning techniques have been widely used in many applications. A big challenge is to automate a planning model, especially for planning applications based on natural language (NL) input. This requires the analysis and understanding of NL text and a general learning technique does not exist in real-world applications. In this article, we investigate an intelligent planning technique for natural disaster management, e.g. typhoon contingency plan generation, through natural language process manuals. A planning model is to optimise management operations when a disaster occurs in a short time. Instead of manually building the planning model, we aim to automate the planning model generation by extracting disaster management-related content through NL processing (NLP) techniques. The learning input comes from the published documents that describe the operational process of preventing potential loss in the typhoon management. We adopt a classical planning model, namely planning domain definition language (PDDL), in the typhoon contingency plan generation. We propose a novel framework of FPTCP, which stands for a Framework of Planning Typhoon Contingency Plan, for learning a domain model of PDDL from NL text. We adapt NLP techniques to construct a ternary template of sentences of NL inputs from which actions and their objects are extracted to build a domain model. We also develop a comprehensive suite of user interaction components and facilitate the involvement of users in order to improve the learned domain models. The user interaction is to remove semantic duplicates of NL objects such that the users can select model-generated actions and predicates to better fit the PDDL domain model. We detail the implementation steps of the proposed FPTCP and evaluate its performance on real-world typhoon datasets. In addition, we compare FPTCP with two state-of-the-art approaches in applications of narrative generation, and discuss its capability and limitations.

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Automated Domain Model Learning Tools for Planning

Cited by 3 publications

References 37 publications

A review of learning planning action models

A review of learning planning action models

Reformulation techniques for automated planning: a systematic review

Learning a Planning Domain Model From Natural Language Process Manuals

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