prof. RNDr. Pavel Surynek, Ph.D.

2023 - 2025

The project focuses on a broad area of basic research on algorithms, concepts and techniques in knowledge engineering. This is a promising and constantly expanding field both in terms of theoretical research and in terms of potential for practical applications. At the same time, the area of the project is experiencing an extreme increase in complexity due to the amount of data and the diversity of techniques that are trying to deal with this trend. The central interest of the project will specifically be methods in data processing, extraction of information-rich data from data and automatic search for explanations for phenomena that occur in the data. A burning problem of current artificial intelligence focused on knowledge processing is the limited ability or inability to provide an explanation leading to a certain decision. As a result, decisions often cannot be traced back to their origin, which makes debugging difficult and reduces the credibility of the system. This question will therefore be reflected throughout the entire project through all sub-themes. It is a doctoral project covering the diverse research interests of the doctoral student members, but providing a general supporting question that intertwines across sub-themes. The project specifically intends to develop algorithms and techniques in neural networks, evolutionary algorithms, image and text processing, and motion information processing, from basic theory to practical applications. The project immediately follows the ending SGS project on modern algorithmic techniques for knowledge engineering.

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.

2022 - 2024

Motion planning for multiple robotic agents (MR-MoP) is a task to find non-colliding sequences of simple movements for individual robotic agents so each agent achieves its individual goal. An important character-istic of the task is the large number of relatively simple robotic agents that can physically interact with each other in various ways. The task is based on the well-known multi-agent path finding (MAPF), but places more emphasis on the real properties of the environment in which the robotic agents operate, namely the continuity of space and time is assumed. Considering the continuity of the environment directly in abstract models can lead to more precise and mode efficient plans. The project assumes algorithmic contributions to motion planning for a multi-agent system on all important layers of common planning abstractions, i.e. from the level of (discrete) classical planning, through (continuous) motion planning, to the execution of plans with physical robots. The new algorithms will be based on the principles of logical reasoning, in particular lazy compilation approaches.

2020 - 2022

The project focuses on research of modern algorithms and techniques in knowledge engineering in a broader sense. The topic of the project represents a promising and constantly expanding area both in terms of theoretical research and in terms of potential for practical applications. In particular, the project will focus on methods in data processing, extraction of information-intensive knowledge from data, and automatic search for explanations for phenomena occurring in the data. The important issue of contemporary artificial intelligence focused on knowledge processing is the limited ability or inability to give explanations leading to a certain decision, and thus often the decision cannot be formally supported and justified. The whole project through all sub-themes will therefore reflect this question. This is a doctoral project covering the diverse research interests of PhD students, but providing a general underlying question that encompasses all sub-themes. The project specifically aims to develop algorithms and techniques in neural networks, evolutionary algorithms, image and text processing, and motion information processing. The project is a direct continuation of a finishing SGS project from data processing area.