Planning Domain Model Research Articles

NaRuto: Automatically Acquiring Planning Models from Narrative Texts

Domain model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Learning action models from narrative texts in an automated way is essential to overcome this barrier, but challenging because of the inherent complexities of such texts. We present an evaluation of planning domain models derived from narrative texts using our fully automated, unsupervised system, NaRuto. Our system combines structured event extraction, predictions of commonsense event relations, and textual contradictions and similarities. Evaluation results show that NaRuto generates domain models of significantly better quality than existing fully automated methods, and even sometimes on par with those created by semi-automated methods, with human assistance.

Temporally extended goal recognition in fully observable non-deterministic domain models

Goal Recognition is the task of discerning the intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (i.e., a sample of the plan executed in the environment). Existing approaches assume that goal hypotheses comprise a single conjunctive formula over a single final state and that the environment dynamics are deterministic, preventing the recognition of temporally extended goals in more complex settings. In this paper, we expand goal recognition to temporally extended goals in Fully Observable Non-Deterministic (fond) planning domain models, focusing on goals on finite traces expressed in Linear Temporal Logic (ltlf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_f$$\\end{document}) and Pure-Past Linear Temporal Logic (ppltl). We develop the first approach capable of recognizing goals in such settings and evaluate it using different ltlf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_f$$\\end{document} and ppltl goals over six fond planning domain models. Empirical results show that our approach is accurate in recognizing temporally extended goals in different recognition settings.

Improving a Planner’s Performance through Online Heuristic Configuration of Domain Models

The separation of planner logic from domain knowledge supports the use of reformulation and configuration techniques, such as macro-actions and entanglements, which transform the model representation in order to improve a planner’s performance. One drawback of such an approach is that it may require a potentially expensive training phase. In this paper, we introduce heuristic approaches for the online configuration of planning domain models. The proposed heuristics consider different aspects of PDDL-encoded operators for reordering such operators in the domain model, relying on the assumption that the way in which operators are encoded carries useful information about their expected use.

Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm

While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to divide the hierarchical planning problem into smaller problems, so-called sub-problems. A pre-processing technique is conducted for each sub-problem to reduce a declarative hierarchical planning domain model and then find an individual solution for each so-called sub-problem sub-plan. In the reduction part, the conflict between sub-plans is resolved to provide a general solution plan to the given hierarchical AI-planning problem. Pre-processing phase helps the planner cut off the hierarchical planning search space for each sub-problem by removing the compulsory literal elements that help the hierarchical planner seek a solution. The proposed approach has been fully implemented successfully, and some experimental results findings will be provided as proof of our approach’s substantial improvement inefficiency.

Refining Process Descriptions from Execution Data in Hybrid Planning Domain Models

The creation and maintenance of a domain model is a well recognised bottleneck in the use of automated planning; indeed, ensuring a planning engine is fed with an accurate model of an application is essential in order that generated plans are effective. Engineering domain models using a hybrid representation is particularly challenging as it requires accurately describing continuous processes, which can have complex numeric effects. In this work we consider the problem of the refinement of an engineered hybrid domain model, to more accurately capture the effect of the underlying processes. Our approach exploits the information content of the original model, utilising machine learning techniques to identify important situation and temporal features that indicate a variation in the original effect. We use the problem of modelling traffic flows in an Urban Traffic Management setting as a case study and demonstrate in our evaluation that the refined domain models provide more accurate simulation, which can lead to higher quality plans. The contribution of this work is a general approach to the automated refinement of hybrid planning domain models that reduces the knowledge engineering effort in producing a detailed process model. The approach can be used for refining the domain model during the initial stages of development, or for re-configuring the domain model when used in the same problem area but with a different scenario. We test out the approach within a real world case study.

Narrative Planning Model Acquisition from Text Summaries and Descriptions

AI Planning has been shown to be a useful approach for the generation of narrative in interactive entertainment systems and games. However, the creation of the underlying narrative domain models is challenging: the well documented AI planning modelling bottleneck is further compounded by the need for authors, who tend to be non-technical, to create content. We seek to support authors in this task by allowing natural language (NL) plot synopses to be used as a starting point from which planning domain models can be automatically acquired. We present a solution which analyses input NL text summaries, and builds structured representations from which a pddl model is output (fully automated or author in-the-loop). We introduce a novel sieve-based approach to pronoun resolution that demonstrates consistently high performance across domains. In the paper we focus on authoring of narrative planning models for use in interactive entertainment systems and games. We show that our approach exhibits comprehensive detection of both actions and objects in the system-extracted domain models, in combination with significant improvement in the accuracy of pronoun resolution due to the use of contextual object information. Our results and an expert user assessment show that our approach enables a reduction in authoring effort required to generate baseline narrative domain models from which variants can be built.

Framer: Planning Models from Natural Language Action Descriptions

In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants' NL input. We demonstrate that our approach is effective at learning models on these tasks.

Domain Model Acquisition in the Presence of Static Relations in the LOP System

This paper addresses the problem of domain model acquisition from only action traces when the underlying domain model contains static relations. Domain model acquisition is the problem of synthesising a planning domain model from example plan traces and potentially other information, such as intermediate states. The LOCM and LOCMII domain model acquisition systems are highly effective at determining the dynamics of domain models with only plan traces as input (i.e. they do not rely on extra inputs such as predicate definitions, initial, final and intermediate states or invariants). Much of the power of the LOCM family of algorithms comes from the assumption that each action parameter goes through a transition. One place that this assumption is too strong is in the case of static predicates. We present a new domain model acquisition algorithm, LOP, that induces static predicates by using a combination of the generalised output from LOCM2 and a set of optimal plans as input to the learning system. We observe that static predicates can be seen as restrictions on the valid groundings of actions. Without the static predicates restricting possible groundings, the domains induced by LOCMII produce plans that are typically shorter than the true optimal solutions. LOP works by finding a minimal static predicate for each operator that preserves the length of the optimal plan.

Acquiring planning domain models using LOCM

AbstractThe problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for artificial intelligence (AI) planning. This paper describes Learning Object-Centred Models (LOCM), a system that carries out the automated generation of a planning domain model from example training plans. The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits assumptions about the kinds of domain model it has to generate, rather than handcrafted clues or planner-oriented knowledge. It assumes that actions change the state of objects, and require objects to be in a certain state before they can be executed. In this paper, we describe the implemented LOCM algorithm, the assumptions that it is based on, and an evaluation using plans generated through goal-directed solutions, through random walk, and through logging human-generated plans for the game of freecell. We analyze the performance of LOCM by its application to the induction of domain models from five domains.

Planning in the Real World

In this article, we describe how real world planning problems can be solved by employing Artificial Intelligence planning techniques. We introduce the paradigm of hybrid planning, which is particularly suited for applications where plans are not intended to be automatically executed by systems, but are made for humans. Hybrid planning combines hierarchical planning – the stepwise refinement of complex tasks – with explicit reasoning about causal dependencies between actions, thereby reflecting exactly the kinds of reasoning humans perform when developing plans. We show how plans are generated and how failed plans are repaired in a way that guarantees stability. Our illustrating examples are taken from a domain model for disaster relief missions enforced upon extensive floods. Finally, we present a tool to support the challenging task of constructing planning domain models.

OAP: AN OBJECT-ORIENTED APPROACH FOR PLANNING MODELING

This article describes a planning approach based on the object representation. A planning domain in OAP (Object-oriented Approach for Planning) consists of a dynamic set of objects. OAP provides a language for planning problems modeling and implementation. This approach can evolve a domain model from a literal (predicative) representation to an object based representation, as well as enhancing the development of planning problems. The goal of OAP is to offer the possibility to design and develop planning problems as any other software engineering problem, and to allow the application of planning to a larger class of domains by using methods (functions) that can be implemented within the world objects. Planning systems using OAP as language can be integrated into any existing object-oriented software with a slight additional effort to transform the system to a planning domain model, which allows the use of planning to solve generic tasks in existing software applications (Business, web,…). Therefore planning in real world systems will be easier to model and to implement using all the software engineering facilities offered by the object-oriented tools.

Engineering and compiling planning domain models to promote validity and efficiency

This paper postulates a rigorous method for the construction of classical planning domain models. We describe, with the help of a non-trivial example, a tool-supported method for encoding such models. The method results in an “object-centred” specification of the domain that lifts the representation from the level of the literal to the level of the object. Thus, for example, operators are defined in terms of how they change the state of objects, and planning states are defined as amalgams of the objects' states. The method features two classes of tools: for initial capture and validation of the domain model; and for operationalising the domain model (a process we call compilation) for later planning. Here we focus on compilation tools used to generate macros and goal orders to be utilised at plan generation time. We describe them in depth, and evaluate empirically their combined benefits in plan generation speed-up. The method's main benefit is in helping the modeller to produce a tight, valid and operational domain model. It also has the potential benefits of (i) forcing a change of emphasis in classical planning research to encompass knowledge-based aspects of target planning domains in a systematic manner, (ii) helping to bridge the gap between the research area of theoretical but unrealistic planning on the one hand, and “scruffy” but real-world planning on the other, (iii) a commitment to a knowledge representation form which allows powerful techniques for planning domain model validation and planning algorithm speed-up can be bound up into a tool-supported environment.