Towards a structured approach to building qualitative reasoning models and simulations

Bredeweg, Bert; Salles, Paulo; Bouwer, Anders; Liem, J.; Nuttle, Tim; Cioaca, E.; Nakova, E.; Noble, Richard; Caldas, Ana Luiza Rios; Uzunov, Yordan; Varadinova, Emilia; Zitek, Andreas

doi:10.1016/j.ecoinf.2007.02.002

Cited by 31 publications

(8 citation statements)

References 5 publications

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“…naturnet.org) and related models (Nakova et al, , 2007, the river Mesta model about dissolved oxygen behaviour has been implemented in the Garp3 workbench , following the framework for qualitative modelling presented in Bredeweg et al (2008).…”

Section: Model Implementationmentioning

confidence: 99%

The river Mesta case study: A qualitative model of dissolved oxygen in aquatic ecosystems

Nakova

Linnebank

Bredeweg

et al. 2009

Ecological Informatics

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Section: Model Implementationmentioning

confidence: 99%

The river Mesta case study: A qualitative model of dissolved oxygen in aquatic ecosystems

Nakova

Linnebank

Bredeweg

et al. 2009

Ecological Informatics

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“…Qualitative simulation is one of effective tools to simulate complex and dynamic systems [1] [2][3] [4]. To make a decision and determine a strategy like a city planning, causal graphbased simulation is one of promising field to analyze without qualitative differential equations [5] [6]. Causal graph model has nodes showing factors in a system and arcs that is connecting with related nodes [7].…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Decision Support System for Sustainable City Planning

Matsuo

Hashiura

Ito

2011

2011 IEEE 13th Conference on Commerce and Enterprise Computing

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In city planning project, it is not easy to make a consensus of people who are living there. Normally, the city design is planned by city officers, councilors and mayer at a first stage, however each citizen has some opinions regarding future of his/her city. Particularly, in recent years, a lot of citizens consider society co-existing with nature in their city plan. In this paper, we show a qualitative simulationbased decision support system for city planning. First, we explain a generalized models and components of qualitative simulation. We show our modeling method to express a complex graph-based qualitative simulation model. Then, we show the process viewer in qualitative simulation-based decision support systems, that is used by citizens in order to understand the future status of the city. Qualitative simulation has an easyto-understanding feature in the process and the result. This paper includes two following contributions; first, we proposed a flexible modeling method to make a qualitative simulation model; second, we proposed methods in which non-specialists of city planning can make easily a model and understand the result of simulations.

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“…During this process the assumptions and features that are essential to represent a domain model are derived from the overall requirements. Within this context we define a planning domain model as follows: This is in line with terminology from general Knowledge Engineering, specific work on domain "theories" of physical systems [4], and what is called the "domain file" in the most common planning domain encoding language, PDDL [26]. In particular, we expect the language in which the domain model is written to have a well-defined syntax and operational semantics: this means that, independent of planner and domain, there is a defined process for executing plans which correspond to sequences of actions in the application domain.…”

Section: The Domain Model In Automated Planningmentioning

confidence: 99%

“…Domain models are often formulated into a planner input language directly from a requirements specification using only basic editors (these are so-called hand-crafted domain models), such as PDDL-studio [29] or the online editor PDDL editor 4 . In some cases, the requirements are encoded firstly into a more applicationoriented language such as UML in itSIMPLE [40], or in AIS-DDL in the KEWI interface [41], and then mapped into the target planner's input language.…”

Section: Model Qualitymentioning

confidence: 99%

“…KEPS can be seen as a special case of knowledge engineering, where the need for methodologies for acquiring, domain modelling and managing formally captured knowledge has long been accepted. It is also related to the area of capturing conceptual knowledge and developing domain models for Qualitative Reasoning in the general Modelling and Simulation area [4]. However, the peculiarities of automated planning and scheduling applications distinguish KEPS from general knowledge-based and simulation systems.…”

Section: Introductionmentioning

confidence: 99%

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Engineering Knowledge for Automated Planning

McCluskey

Vaquero

Vallati

2017

Proceedings of the Knowledge Capture Conference

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Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domainindependent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers. This paper raises some issues relating to the engineering of application knowledge for automated planning, focussing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the automated planning community in order to improve tools for supporting the engineering of planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.

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Towards a structured approach to building qualitative reasoning models and simulations

Cited by 31 publications

References 5 publications

The river Mesta case study: A qualitative model of dissolved oxygen in aquatic ecosystems

The river Mesta case study: A qualitative model of dissolved oxygen in aquatic ecosystems

A Qualitative Decision Support System for Sustainable City Planning

Engineering Knowledge for Automated Planning

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