Validation and verification issues in a timeline-based planning system

2010 ◽  
Vol 25 (3) ◽  
pp. 299-318 ◽  
Author(s):  
Amedeo Cesta ◽  
Alberto Finzi ◽  
Simone Fratini ◽  
Andrea Orlandini ◽  
Enrico Tronci
Keyword(s):  
Resource Constraints ◽  
Knowledge Engineering ◽  
Original System ◽  
Planning System ◽  
Direct Access ◽  
Validation And Verification ◽  
Planning And Scheduling ◽  
Evaluation Functions ◽  
Engineering Environment ◽  
Effective Use

AbstractTo foster effective use of artificial intelligence planning and scheduling (P&S)systems in the real world, it is of great importance to both (a) broaden direct access to the technology for the end users and (b) significantly increase their trust in such technology. AutomatedP&Ssystems often bring solutions to the users that are neither ‘obvious’ nor immediately acceptable to them. This is because these tools directly reason on causal, temporal, and resource constraints; moreover, they employ resolution processes designed to optimize the solution with respect to non-trivial evaluation functions. Knowledge engineering environments aim at simplifying direct access to the technology for people other than the original system designers, while the integration of validation and verification (V&V) capabilities in such environments may potentially enhance the users’ trust in the technology. Somehow,V&Vtechniques may represent a complementary technology, with respect toP&S, that contributes to developing richer software environments to synthesize a new generation of robust problem-solving applications. The integration ofV&VandP&Stechniques in a knowledge engineering environment is the topic of this paper. In particular, it analyzes the use of state-of-the-artV&Vtechnology to support knowledge engineering for a timeline-based planning system called MrSPOCK. The paper presents the application domain for which the automated solver has been developed, introduces the timeline-based planning ideas, and then describes the different possibilities to applyV&Vto planning. Hence, it continues by describing the step of addingV&Vfunctionalities around the specialized planner, MrSPOCK. New functionalities have been added to perform both model validation and plan verification. Lastly, a specific section describes the benefits as well as the performance of such functionalities.

Planning meets verification and validation in a knowledge engineering environment

2014 ◽  
Vol 8 (1) ◽  
pp. 87-100 ◽  
Author(s):  
Andrea Orlandini ◽  
Giulio Bernardi ◽  
Amedeo Cesta ◽  
Alberto Finzi
Keyword(s):  
Knowledge Engineering ◽  
Verification And Validation ◽  
Engineering Environment

Knowledge-Intensive Evolutionary Algorithms for Solving a Healthcare Fleet Optimization Problem

2018 ◽  
pp. 192-223
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Fitness Functions ◽  
Evaluation Functions ◽  
Mutation Operators ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Crossover And Mutation

In this chapter, the authors show how knowledge engineering techniques can be used to guide the definition of evolutionary algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. Various representations of the fitness functions, the genome, and mutation/crossover operators adapted to different types of problems (routing, scheduling, etc.) have been proposed in the literature. However, real problems including specific constraints (legal restrictions, specific usages, etc.) are often overlooked by the proposed generic models. To ensure that these constraints are effectively considered, the authors propose a methodology based on the structuring of the conceptual model underlying the problem, as a labelled domain ontology suitable for optimization by EA. The authors show that a precise definition of the knowledge model with a labelled domain ontology can be used to describe the chromosome, the evaluation functions, and the crossover and mutation operators. The authors show the details for a real implementation and some experimental results.

scholarly journals Preface to special issue on planning and scheduling

2010 ◽  
Vol 25 (3) ◽  
pp. 247-248
Author(s):  
Roman Barták ◽  
Amedeo Cesta ◽  
Lee McCluskey ◽  
Miguel A. Salido
Keyword(s):  
Constraint Satisfaction ◽  
Real World ◽  
Knowledge Engineering ◽  
Real Life ◽  
Ai Planning ◽  
Planning And Scheduling ◽  
Useful Knowledge ◽  
State Dependent ◽  
Life Problems ◽  
Modelling Techniques

AbstractPlanning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI) with broad practical applicability. Many real-world problems can be formulated as AI planning and scheduling (P&S) problems, where resources must be allocated to optimize overall performance objectives. Frequently, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays an important role in solving such real-life problems, and integrated techniques that manage P&S with constraint satisfaction are particularly useful. Knowledge engineering supports the solution of such problems by providing adequate modelling techniques and knowledge extraction techniques for improving the performance of planners and schedulers. Briefly speaking, knowledge engineering tools serve as a bridge between the real world and P&S systems.

Study and Execution of Advanced Batch Production Planning System for an Aerospace Industry

2009 ◽  
Vol 16-19 ◽  
pp. 748-752
Author(s):  
Muhammad Younus ◽  
Yong Yu ◽  
Hu Lu ◽  
Yu Qing Fan
Keyword(s):  
Production Planning ◽  
Manufacturing Industries ◽  
Planning System ◽  
Software System ◽  
Batch Production ◽  
Planning And Scheduling ◽  
Scheduling System ◽  
Production Planning And Scheduling ◽  
Advance Production ◽  
Work Orders

Modern manufacturing industries are increasingly faced with international competition and fluctuating market conditions in the age of globalization. In striving to remain competitive, manufacturing industries must deliver products to customer at the lowest cost, at the best quality and in the minimum lead time. As a result, it becomes mandatory to design and implement the advance production planning and scheduling system that supports shorter product cycles despite more complex and specialized manufacturing processes. The advance production planning and scheduling system provides the leaner production strategies and real time information throughout the industry. This paper presents an appropriate Advance Production Planning and Scheduling Software System for a batch production aerospace manufacturing industry. The Software system receive the customer‘s order and perform material requirement planning using software. The software system sends procurement requisition to the procurement department for the materials which is not available in the store for manufacturing of parts. It issues release order to the store section for the issuance of material to the manufacturing shops. It also issues weekly and daily plan with production schedule to the manufacturing shops. It also issues work orders to the production shops for the manufacturing and assembly of the parts. On the completion of the product assembly, proper closing of the work orders has to be done and product may deliver to the warehouse for further handing over to the customers. In case of modification of order by the customer, the Software System will automatically update the relevant data.

scholarly journals EVALUATION OF THE GENERAL ATTRACTIVENESS OF THE URBAN PUBLIC SPACE ACCORDING TO MANY CRITERIA

2020 ◽  
Vol 2 (2) ◽  
pp. 193-203
Author(s):  
Feshchur R. ◽  
◽  
Sosnova N. ◽  
Keyword(s):  
Public Space ◽  
Resource Constraints ◽  
Public Spaces ◽  
Planning System ◽  
Space Planning ◽  
Urban Public Space ◽  
Controversial Problem ◽  
Reasonable Choice ◽  
The City

Cities are constantly changing – new and existing facilities are created and reconstructed, existing ones are modernized, and new territories are developed, and, accordingly, public spaces are formed and develop in a certain way. To a large extent, this process is random and does not take place systematically, but this rather happens as a response to the urgent economic, environmental, social or other needs of city residents. Development management in the urban planning system is designed to solve the controversial problem of maintaining integrity and at the same time striving for its transformation. The use of the tools of mathematical modeling, considered in the article, allows one to solve the problems of spatial development of a city and its public spaces in a purposeful way, and to coordinate such a solution with the interests of stakeholders. When forming public spaces of a city one faces the task of streamlining competing development projects (alternative projects) for a particular area of ​​a city, taking into account the importance of their impact on the establishment of a distinctive image of the city and ensuring quality of life of its residents. To solve this problem, it is advisable to use methods of expert evaluation of design decisions, in particular, methods of ranking, valuating, and folding vector-valued criterion into a scalar criterion (integrated indicator of project weight). Ranking means assignment of a certain rank (a number from the natural series) to every project. The most important project is given the highest rank, which corresponds to number "one". The sum of the ranks given by all experts to a particular project can be considered as a generalized value of its weight. The article considers approaches to the assessment of urban public spaces on the basis of various criteria, namely urban, social, economic, environmental ones. The developed models of public space planning are designed for making a reasonable choice from a set of alternative projects subject to implementation, either according to the dominant criterion or according to many criteria in the conditions of resource constraints.

Pillars of Ontology Treatment in the Medical Domain

2009 ◽  
Vol 11 (4) ◽  
pp. 47-73 ◽  
Author(s):  
Daniel Sonntag ◽  
Pinar Wennerberg ◽  
Paul Buitelaar ◽  
Sonja Zillner
Keyword(s):  
Large Scale ◽  
Knowledge Engineering ◽  
Semantic Annotation ◽  
Image Data ◽  
Clinical Decision ◽  
Direct Access ◽  
Seamless Integration ◽  
Data Repositories ◽  
Semantic Image Retrieval ◽  
Medical Domain

In this chapter the authors describe the three pillars of ontology treatment in the medical domain in a comprehensive case study within the large-scale THESEUS MEDICO project. MEDICO addresses the need for advanced semantic technologies in medical image and patient data search. The objective is to enable a seamless integration of medical images and different user applications by providing direct access to image semantics. Semantic image retrieval should provide the basis for the help in clinical decision support and computer aided diagnosis. During the course of lymphoma diagnosis and continual treatment, image data is produced several times using different image modalities. After semantic annotation, the images need to be integrated with medical (textual) data repositories and ontologies. They build upon the three pillars of knowledge engineering, ontology mediation and alignment, and ontology population and learning to achieve the objectives of the MEDICO project.

Pillars of Ontology Treatment in the Medical Domain

2011 ◽  
pp. 162-186 ◽  
Author(s):  
Daniel Sonntag ◽  
Pinar Wennerberg ◽  
Paul Buitelaar ◽  
Sonja Zillner
Keyword(s):  
Large Scale ◽  
Knowledge Engineering ◽  
Semantic Annotation ◽  
Image Data ◽  
Clinical Decision ◽  
Direct Access ◽  
Seamless Integration ◽  
Data Repositories ◽  
Semantic Image Retrieval ◽  
Medical Domain

In this chapter the authors describe the three pillars of ontology treatment in the medical domain in a comprehensive case study within the large-scale THESEUS MEDICO project. MEDICO addresses the need for advanced semantic technologies in medical image and patient data search. The objective is to enable a seamless integration of medical images and different user applications by providing direct access to image semantics. Semantic image retrieval should provide the basis for the help in clinical decision support and computer aided diagnosis. During the course of lymphoma diagnosis and continual treatment, image data is produced several times using different image modalities. After semantic annotation, the images need to be integrated with medical (textual) data repositories and ontologies. They build upon the three pillars of knowledge engineering, ontology mediation and alignment, and ontology population and learning to achieve the objectives of the MEDICO project.

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