Jaan Penjam

Applications of Structural Synthesis of Programs

This is an experience report on the automatic and hidden usage of program synthesis in several application domains. The structural synthesis of programs has been implemented in an object-oriented programming environment NUT and used for development of simulation software, engineering calculations software, implementing a benchmark for safety critical systems and development of highly interactive visual modeling of radar coverage of landscape.

Semi-automated Generation of DSL Meta Models from Formal Domain Ontologies

This paper addresses the problem of alignment of domain ontologies and meta-models of Domain Specific Languages DSL in order to facilitate the DSL development process by formal methods. The solution presented in this paper automatically generates design templates of a DSL meta-model that are consistent with a given domain ontology represented in OWL DL. Consistency of alignment is ensured by predefined mapping rules between constructs of ontology modelling language OWL DL and a modelling language used for representing DSL meta-models. The approach is implemented as an extension to the CoCoViLa system and the CoCoViLa modelling language is used for representing DSL meta-models. The evaluation of the provided method is carried out by developing the DSL for the IT risk analysis and management domain.

Ontology-based design of surveillance systems with NUT

This paper covers ontology-based programming, using the NUT language as a notation for the semantics of domain knowledge. A specification method and problem-solving techniques are demonstrated on an example of modeling and management of a radar surveillance system in order to find the optimal disposition and configuration of equipment. The structural synthesis of programs - a technique that is essential for domain knowledge handling, is briefly discussed.

Developing a Lean Mass Customization Based Manufacturing

We describe an experimental mass customization based manufacturing system which relies on a sophisticated IT infrastructure and CAE to produce novel wooden design products in a lean and flexible way. The main focus of this paper is on the IT infrastructure where several AI techniques for machine vision, search and planning are applied. The IT system has a service oriented architecture and is composed of heterogeneous distributed components communicating via custom web services. A key component of this system is an smart optimizer which helps to improve warehouse logistics, material utilization and speeds up manual creative work.

Constraints in NUT

NUT is a programming system for knowledge-based programming with facilities for automatic program synthesis. The system allows to specify computational problems in OO style. Concepts and objects can be treated in NUT as functional constraint networks. Constraint satisfaction problems are solved using algorithms for automatic program synthesis described earlier for the PRIZ system.

Differential evolutionary approach guided by the Functional Constraint Network to solve program synthesis problem

In this paper we consider two different research areas — Constraint Satisfaction Problems and Evolutionary Methods — in the terms of program synthesis. The paper discusses how a Functional Constraint Network could guide Differential Evolution search for achieving an effective program synthesis system. We propose an evolutionary approach for program synthesis from a formal relational specification that is augmented by input-output examples of the desired program behavior.

Program Construction in the Context of Evolutionary Computation

Many optimization algorithms that imitate certain principles of nature have been proven useful in various application domains. The following paper shows how Evolutionary Algorithm (EA) can be applied to model (program) construction for solving the discrete time system identification problem. Non-linear system identification is used as an example problem domain for studying possibilities of EA to discover the relationship between parameters in response to a given set of inputs.