Integration of Tools for Intelligent Technologies
Supported by Ministry of Education, Slovak Republic,
Grant No. 1/5032/98
The project concentrates on two ways of integration of tools developed in frame of different fields of Artificial
Intelligence, these are:
The project comprises also development of new or modified methods in different fields of AI with emphasis on
possibility of their integration for complex tasks solving. The project will concentrate on Neural Networks,
Evolutionary Algorithms, Artificial Life and Constraint Logic Programming.
- including of different AI tools into one integral programming tool
- integration of AI tools into architecture of multi-agent environment
Present state of the subject
In the field of Intelligent Technologies two big groups of means and methods have developed:
These two different directions in AI have been for a long time presented as two isolated parts of AI and also
inside these directions the research is done in a few considerably different fields. It is getting more and more obvious
that for solution of complex tasks it is advantageous to make use of combined methods and procedures.
- symbolic means and methods of AI
- sub-symbolic means and methods of AI
One of possible ways is to design an architecture and structure of communication among heterogeneous
distributed agent-based tools of AI so as to secure the solution of highest possible quality in acceptable time period
using up minimal computational resources
Design-to-time is an approach for solution of problems in fields, where the following conditions have been
fulfilled: 1. there is not sufficient time to find a complete (or optimal) solution, 2. a time limit is defined for solution
search, 3. there is more than one applicable methods distinguishing in quality of solution found and in the
computation resources needed, 4. also non-optimal solution is acceptable, 5. it is possible to predict time and
resources needed to find the solution, although the level of predictability for new tasks may be low
These characteristics are in many respects equal with real-time problems where there is not enough time to find
optimal solution. Depending on time and available resources it is possible to choose a suitable means that will find a
suboptimal solution under given conditions simultaneously minimizing the load on other resources.
Although different approaches for solution of this class of tasks have been applied (anytime algorithms, imprecise
computation), there exists no general methodology yet. Approaches exploiting multi-agent technologies show up to
be promising at this point.
Another way in exploiting seemingly completely heterogeneous methods of AI is to include different tools of
intelligent technologies into one integral tool that will be applied for solution of real-life problem. Currently there is a
few of such tools integrating different AI methods. One of interesting products of Siemens company is programmers
version of integrating programming system ECANSE for operating system WINDOWS NT. This tool is suitable for
integration of AI methods such as CLP method (Constraint Logic Programming), Neural Networks, Fuzzy
Regulator, Genetic Algorithms etc. in order to solve given real-life problem.
In last few years constraints satisfying programming systems integrating very effective algorithms of propagation
and constraints satisfaction are getting into the front and they present very interesting method for solving and
prototyping of whole scale of real-life applications mainly because of their declarative characteristics and increasing
efficiency. They represent modern programming systems based on constraint logic programming or object-oriented
and distributed programming systems. Some of these systems in spite of their short history have become
commercially very successful. But these systems are far too expensive regarding our current possibilities. There is
also a group of systems available free of charge mostly for academic purposes and their performance is roughly
comparable to the commercial systems. These systems present excellent development tool which is possible to exploit
for pedagogical purposes as well as for purposes of development of different applications and testing of new methods.
And just the AI tools designed using these development tools are suitable for inclusion into integrated or multi-agent
Biologically motivated methods of AI represent one of key groups of tools we are intending to integrate in frame
of this project. But these methods alone are often based on co-operation of huge amount of elements and due to
emergency the integral tool obtains abilities exceeding the sum of abilities of the integrated elements. These
phenomena are studied by Artificial Life (ALIFE).
Evolutionary algorithms present a class of searching algorithms inspired by natural processes. They apply
population principle to searching for the solution. This principle enables concurrent searching through the space of
potential solutions. Their properties predestine them for solving of tasks not requiring the guarantee of finding the
optimal solution. Ability to solve also problems the complexity of which is beyond the frontiers of applicability of
full algorithms along with potential to find alternative solutions and solutions based on more goals makes a
promising means of solution of broad range of tasks from technical praxis out of this class of algorithms. Broader
application of evolutionary algorithms is hindered by relatively complex process of design needed to create an
efficient implementation. In the present a typical design process consists of creation of ad-hoc variant of the
algorithm and its gradual empirical improvement selecting alternative parts of algorithm, setting more suitable
values of control parameters, or implementing domain knowledge.
Particular contribution expected
We consider the main contribution of the project to be development of two types of environment allowing
integration of heterogeneous methods of artificial intelligence:
Development of integral system for application of different means of AI will also enable more effective utilization of
means and methods of AI for solution of real-life problems. Also finding of solution of some partial tasks aiming at
creation of AI tools suitable for inclusion into the above mentioned environments may be considered to be
contribution of this project. These partial tasks include:
- design and implementation of architecture and structure of communication among heterogeneous computing
tools for solving of problems of design-to-time type and its tests on solving of scheduling problems,
- design and implementation of new modules and units of interconnection for inclusion of heterogeneous means
and methods of AI into integrated environment ECANSE NT.
- design, implementation and tests of some new algorithms in the environment of programming and constraints
satisfaction where for the test purposes randomly generated tasks as well as real-life scheduling applications will be
- development of complete methodology for effective solution of scheduling applications and its application for
chosen scheduling tasks,
- agentification of developed methods for purposes of utilization of CLP technology in the multi-agent system of
- design and implementation of modules of intercommunication between Stuttgart Neural Networks Simulator
and ECANSE NT system in the environment of operating system UNIX on one side and WINDOWS NT on the
- simplification of the process of application of evolutionary algorithms for solution of some real-life classes of
technical problems by means of providing of suitable tools and methods of design and adaptation of these algorithms
to the requirements of optional compatibility with other AI tools,
- design and implementation of autonomous agents in the environment of Tierra simulator and as well as
utilization of knowledge from this field in development of co-operating AI tools.
Proposal of the ways how to reach the project goals
Regarding development of multi-agent environment we propose following consecutive steps:
Regarding development of integrated programming environment we plan to take following steps:
- in the first year we plan to concentrate on analysis of requirements and estimation of resources needed to find
solution using concrete method and on design and implementation of algorithm for estimates computation,
- in the next stage of project we plan to design architecure and communication interface for individual agentified
computation methods. Implemented algorithms will be checked on a class of test applications and real-life
In development of methods and tools based on Constraint Logic Programming we will go through following stages:
- design and implementation of individual modules into programmers version of ECANSE NT,
- development of required programming equipment in UNIX OS for the needs of communication interface.
In the field of design and application of evolutionary algorithms we will concentrate on gradual building up of
suitable development environment for design of applications of evolutionary algorithms which would allow quick
prototypical design with possibility of empirical evaluation of more variants of solution, of optimization of the
performance of algorithm and consecutive generation of code for the target environment.
- in the first year of the project we will concentrate mainly on solving of scheduling tasks in the CLP
environment and on design of methods for support of solution of these tasks in CLP; designed methods will be tested
- in the next stage of the project we will concentrate on agentification of developed methods and of CLP
technology as such, on formulation of basic premises and of interface for integration into multi-agent system,
- in the last stage we plan to agentify chosen methods of CLP to as broad extent as possible for their utilization in
the environment of multi-agent system of intelligent technologies.
In study of artificial life and application of obtained knowledge we will proceed in following steps:
- implementation of Tierra simulator and corresponding graphical interfaces,
- study of behavior of autonomous agents in different internal algorithms,
- utilization of obtained knowledge in design and implementation of real-life application.