Concept of a Robot Computational Chemist Geerd HF Diercksen - - PowerPoint PPT Presentation

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Concept of a Robot Computational Chemist Geerd HF Diercksen - - PowerPoint PPT Presentation

Introduction Architecture Implementation Summary and outlook Concept of a Robot Computational Chemist Geerd HF Diercksen Max-Planck-Institut fr Astrophysik Garching, GERMANY November 27, 2007 Geerd HF Diercksen EDS Introduction


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Introduction Architecture Implementation Summary and outlook

Concept of a Robot Computational Chemist

Geerd HF Diercksen

Max-Planck-Institut für Astrophysik Garching, GERMANY

November 27, 2007

Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook

Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Implementation

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Summary and outlook Summary Acknowledgement

Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

Munich software

The Munich software system was designed, implemented and in use from the late sixties and has served computational studies in theoretical chemistry for about 30 years. The software was characterised by: labelled (named) data sets that can easily be located on sequensial data sets.

Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

OpenMol software

The OpenMol (Open Molecular) Program was designed, and implemented and is in use since the early ninetieth. The software is characterised by: the use of abstract data types (within a Fortran environment) and modularisation based on abstract data types (J. Guttag, 1977).

Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

Motivation

The ultimate goal in designing OpenMol was to build knowledge-based software in computational chemistry to provide the novice (non-expert user) in academia and particularly in industry with guidance in choosing physically correct models which are amenable to computation in order to avoid results of unspecified reliability.

Geerd HF Diercksen EDS

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History

Geerd HF Diercksen Concepts of a Knowledge-based Simulation Environment in Physics in: Supercomputers, Algorithms, and Scientific Computation, FA Matsen and T. Tajima, (ed.), University of Texas Press, Austin, 1986 Geerd HF Diercksen and George G Hall Intelligent software: The OpenMol program Computers in Physics 8, 215 (1994) Geerd HF Diercksen Artificial intelligence in computational chemistry Research Grant Application to the Deutsche Forschungsgemeinschaft 17 July 2000

Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Summary and outlook Summary Acknowledgement

Geerd HF Diercksen EDS

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Overall objective

The objective of the Robot Computational Chemist (RCC) project is to develop and implement an artificial intelligence approach to planning, conducting, and understanding

  • f computational studies in quantum chemistry.

Geerd HF Diercksen EDS

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

The Question

A Question put to the Robot Computational Chemist must contain the following minimum information about the subject: the quantum system to be studied, the property to be calculated, and the accuracy of the property.

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Introduction Architecture Implementation Summary and outlook Background Objectives Requirements

Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Overall requirements

The requirements follow very much the steps a researcher would/should follow. They may be grouped according to the three objectives of the Robot Computational Chemist: planning, conducting, and understanding

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Planning

Requirements: checking the domain of knowledge of the subject, checking for previous studies on the subject, determining the necessity for a new calculation,

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Planning

continued: selecting a suitable theoretical model, selecting the method for solving the model, selecting the software required, determining the computer resources required, checking for the availability of the required soft- and hardware resources, estimating the feasability of the calculation.

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Conducting

Requirements: compiling/editing the software to be used, compiling/editing the input to the software, submitting the calculation, controlling the excecution of the calculation, compiling the results of the calculation.

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Understanding

Requirements: checking the assumptions made in selecting the method and basis set, comparing the results to previous theoretical and experimental results. evaluating the reliability and accuracy of the results, updating the general background knowledge and case base.

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Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Implementation

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Summary and outlook Summary Acknowledgement

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Summary

The basis for the Robot Computational Chemist is an Evolutionary Decision Support (EDS) system. The EDS architecture will be built based on a flexible methodology for knowledge-intensive case-based reasoning. Key to this approach will be the integration of general background knowledge with specific problem-solving experience from computational chemistry.

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Case-based reasoning

CBR is a problem-solving model that is concerned with solving new problems by adapting solutions that worked for similar problems in the past. The key requirements for a case-based approach to knowledge-intensive problem-solving are: a library of cases, which capture specific problem-solving experience, knowledge structures that allow the identification of the most relevant cases in the light of the new problem, and knowledge structures that transfer the solution from the most relevant case(s) to the new problem.

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Processes

Knowledge-intensive problem-solving involves the following processes: capturing and representation of incomplete and imprecise knowledge, fusion of heterogeneous information, integration of contextual and general background knowledge, combination of multiple-expert case bases, automatic construction of case bases from data,

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Processes

continued: discovery of relevant knowledge in databases, discovery of case clusters for modelling and interpreting case retrieval, usage of soft computing techniques for learning and construction of case retrieval knowledge structures, and knowledge management.

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Strategic objectives

Three strategic objectives will serve as principal guidelines during the course of the project: repository of general background knowledge, repository of specific problem-solving experience, and global access and universal information system.

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EDS components

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Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Implementation

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Summary and outlook Summary Acknowledgement

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Repository of background knowledge

The repository of background knowledge captures general background knowledge from computational chemistry. Such knowledge is independent from any specialised problem-solving or decision-making knowledge. This approach allows to use the knowledge repository in a variety of ways.

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Repository of background knowledge

The repository of background knowledge provides the basis for efficient and effective mechanisms for retrieval of relevant information such as knowledge artefacts (e.g. cases and rules) in a knowledge-based system or documents on the Web, systems used in education and training, and a knowledge navigation system that experts and novices can use directly to explore concepts in chemistry.

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Repository of specific problem-solving experience

The repository of specific problem-solving experience captures specific problem solving episodes or cases and provides suitable query and retrieval mechanisms. Using analogy-based access methods and knowledge-based adaptation techniques, the cases in this repository may be re-used in a wide variety decision-making scenarios. Crucial for the effective re-use of this knowledge source will be its integration with the more general knowledge structures of the repository of background knowledge.

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Global information system

Initially, the global access and universal information system will be implemented as a localised, isolated decision support facility. In future, it is intended to extend the systems basic architecture so that its knowledge sources (general and specific) can be embedded as so-called infohabitants in a future universal information system. This vision includes passive access (decision support) as well as largely autonomous forms of pattern formation and self-organisation such as concept learning.

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EDS components

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Outline

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Introduction Background Objectives Requirements

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Architecture Outline Components Processes

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Implementation

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Summary and outlook Summary Acknowledgement

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Objective

The EDS framework describes the processes involved in problem-solving through a highly modular architecture of knowledge structures and repositories, such as general, specific, retrieval, adaptation knowledge, ....

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Objective

The core processes reflected in the EDS architecture include: retrieval of relevant knowledge artefacts such as problem-solution episodes, ontology partitions, rules, etc., transfer or adaptation of the information contained in these artefacts in the light of the new problem, evaluation of the proposed course of action or solution, and integration of the new problem-solving episode into the knowledge repositories. A key concept in this architecture is that these processes may take place in an iterative fashion permitting the evolution of a solution.

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EDS components

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General

The implementation is organized into: 3 tracks and 6 workpages.

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General

Critical to the project will be a structured approach to information and knowledge modelling with a clear concept for modularity and openness (universal information system). Therefore,

  • bject-oriented tools and techniques and

the CommonKADS methodology will be used as key methodical backbone for the project to ensure a systematic development.

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General

The 3 tracks are: acquisition, capturing, and encoding of actual knowledge structures (general and specific knowledge components), development of the generic knowledge repositories and access protocols, development of the generic EDS engine. Because of the decoupling of knowledge repositories and decision processes, some of the development will initially proceed in parallel along three main tracks.

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General

The 6 work packages address the following topics: problem characterisation and initial knowledge acquisition, knowledge acquisition and engineering: general knowledge, knowledge repositories and access, evolutionary decision support engine, integration of knowledge into repositories of the overall system, integration of numerical software.

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General

The work package Knowledge acquisition and engineering: general knowledge breaks down into 4 sub-packages: knowledge repository: background knowledge, knowledge repository: case library, knowledge repository: retrieval knowledge, knowledge repository: adaptation knowledge structures.

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Problem characterisation and initial knowledge acquisition

This work package is concerned with identifying and describing the key knowledge and information areas, structures and resources (cases, concept and problem spaces, ontologies, databases, etc.) needed to develop a robust and competent system. This phase will see a very close and intensive collaboration between the domain experts and the knowledge engineers.

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Knowledge acquisition and engineering: general knowledge

This work package is concerned with eliciting, capturing, and encoding of the knowledge needed to populate the various knowledge repositories. Based on the initial knowledge acquisition effort, key activity in this work package will be concentrated on the acquisition of detailed general and specific knowledge structures used within the chosen subset of computational chemistry.

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Knowledge repositories and access

This work package is concerned with designing, implementing and populating the actual knowledge repositories and access protocols using adequate design and implementation methodologies, platforms, and languages. The methodology used in this work package will be strongly based on object-oriented analysis, design and implementation principles, tools and languages.

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Evolutionary decision support engine

This work package is concerned with designing and implementing the generic EDS engine using adequate design and implementation methodologies, platforms, and languages. Key tasks within this package are concerned with the development of algorithms that support the main processes of the EDS system: retrieval, adaptation, evaluation, and integration.

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Integration of knowledge into repositories of the overall system

This work package is concerned with integrating the acquired knowledge into the corresponding repositories of the EDS engine, and test and evaluate the resulting system.

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Integration of numerical software

This work package is concerned with integrating existing numerical software used in computational chemistry into the prototype system, in particular the OpenMol software specifically developed for this purpose.

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Knowledge acquisition - Knowledge repository: background knowledge.

This sub-package is concerned with identifying, capturing, characterising, and encoding an adequate subset of general knowledge types and concepts (ontologies, guidelines, computation parameters, rules, classes, etc.) from chemistry that will form the basis for the system.

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Knowledge acquisition - Knowledge repository: case library

This sub-package is concerned with identifying (generate if necessary), capturing, characterising, and encoding an adequate set of concrete problem-solving episodes or cases, which can be understood (described) in sufficient detail by the body of available general knowledge. This requires the development of a suitable case representation format that captures the knowledge necessary to derive answers put to the system, and is amenable to the general EDS process model.

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Knowledge acquisition - Knowledge repository: retrieval knowledge

This sub-package is concerned with identifying, capturing, and encoding adequate knowledge structures that will reliably and efficiently identify full or partial cases within the case library given a set of descriptors.

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Knowledge acquisition - Knowledge repository: adaptation knowledge structures

This sub-package is concerned with identifying, capturing, and encoding adequate knowledge structures that will reliably and efficiently analyse and modify (adapt) the retrieval results at intermediate steps of the evolutionary decision process. These structures will also hold the knowledge required for evaluating the intermediate results (decisions, solutions)

  • btained in the various decision processes.

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Introduction Architecture Implementation Summary and outlook Summary Acknowledgement

Outline

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Introduction Background Objectives Requirements

2

Architecture Outline Components Processes

3

Implementation

4

Summary and outlook Summary Acknowledgement

Geerd HF Diercksen EDS

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Similarity

The open question in this project that needs some basic research efforts concerns similarity: What really defines similarity of quantum systems with respect to the methodology suitable for studying them?

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Summary

The development and implemention of a local pilot version of restricted scope of the EDS was layed out as

a 2 year project for 2 post-doctoral fellows.

The refrees of the DFG argued that time and manpower is underestimated considering the objectives of the project. Probably they are right.

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Outline

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Introduction Background Objectives Requirements

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Implementation

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Individuals

Major contributions to this project over the past two decades by the following colleagues are greatefully acknowledged: Werner Dubitzki Wlodek Duch Jacek Karwowski Al Matsen Stan Scott Mike Zerner

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Acknowledgement: MPI für Astrophysik

Geerd HF Diercksen EDS