Evolving Language Ecosystems
2016 - 2022
The Evolving Language Ecosystems project explores the fundamental techniques and algorithms for evolving programming languages and their ecosystems. Our purpose is to reduce the cost of wide-ranging language changes and obviate the need for devising entirely new languages. Our findings will grant both researchers and practitioners a greater degree of freedom when experimenting with new ideas on how to express computation.
Big Code: Scalable Analysis of Massive Code Bases
2019 - 2022
The project aims to create at the FIT CTU the Institute of Scalable Code Analytics (ISCA), the first research centre in the CR focused on analyses of large code bases available on the Internet. Software systems are written in source code; BigCode refers to the massive codebases on the Internet. Combining techniques from programming languages and statistical machine learning will allow the mining of these codebases for crucial insights. The requested funds will be invested, in part, to provide the FIT with the first hardware and software infrastructure for big code data analysis. The other part of the research funding will attract internationally renowned researchers in the field of computer languages. The team is synergistic with existing research capacities at the FIT in software and knowledge engineering, data mining and parallel computing. The new team is well connected internationally and will bring investment from leading industrial partners that include Google and Oracle.
Intelligent Algorithms for Generalized Variants of Multi-Agent Path Finding
2019 - 2021
Multi-agent path finding (MAPF) is a task of finding non-colliding paths for multiple distinguishable agents in a graph. The MAPF problem represents an important theoretical challenge but also has many practical applications. Solving techniques for the standard MAPF experienced a significant progress recently for both the optimal and the sub-optimal case. This project reflects the growing interest of research community in generalizations of MAPF. Our research aims on study of intelligent solving algorithms in several diverse conceptual directions of MAPF generalizations that are unique to this project. Generalizations in logic formulations of MAPF with focus on expressing MAPF in the SAT modulo theory framework (SMT) and complex local and global constraints are studied. In adversarial variants of MAPF (AMAPF), where multiple teams of agents compete in reaching their goals, the project aims on combination of game-theoretic approach with machine learning. Worthwhile generalizations concern polynomial-time algorithms for MAPF where extensions from undirected to directed graphs are studied.