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Automated Planning

Automated planning solving real-world problems ↓
Automated Planning

We focus on the research of basic principles of classical planning and heuristics, but also on variants such as multi-agent planning and on the application of planning to real-world problems. The ability to plan a sequence of actions in order to reach the desired goal is one of the basic manifestations of intelligence, both natural and artificial. It comes as no surprise that planning has been studied in AI since its very beginning in the 60's and 70' with the STRIPS language and the well known A* algorithm. Both are used up to date, but with significant improvement in automated translation, invariant synthesis and automatically derived heuristics exploiting the problem structure. And that's where we come in!

Research and Thesis Topics

Classical Planning
  • Use of invariants in optimal planning (graph theory, complexity theory)
  • Integration of domain-specific solvers to improve the efficiency of classical planning on particular domains (puzzles, logistics, videogames)
  • The use of machine learning techniques in planning and extraction of structural information (machine learning, deep learning, neural networks)
  • Plan optimization
Multi-Agent Planning
  • Measuring and reducing privacy leakage (secure multiparty computation)
  • Developing a privacy-preserving planner (cryptography, security)
  • Optimal multi-agent planning based on finite-state machine intersection
Temporal Planning
  • Reasoning about time and resources
  • Urban Traffic Control

Research Results

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Hacking
human future

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