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Seminar: Advanced Topics in AI Planning - Topics
Some articles may only available from within the university network. To download, you must use a university computer or VPN.Area A: Symbolic Planning
A1: Transition Trees for Cost-Optimal Symbolic Planning
Betreuer:
Dr. David Speck
- Transition Trees for Cost-Optimal Symbolic Planning (PDF)
A2: Symbolic Heuristic Search using Decision Diagrams
Betreuer:
Dr. David Speck
- Symbolic Heuristic Search using Decision Diagrams (PDF)
A3: Relaxed exists-step encoding
Betreuer:
Dr. Gregor Behnke
- Planning as Satisfiability with Relaxed ∃-Step Plans (PDF)
A4: Planning with Axioms
Betreuer:
Dr. David Speck
Area B: Epistemic Planning
B1: Epistemic Planning as Classial Planning
Betreuer:
Dr. Thorsten Engesser
- Beliefs In Multiagent Planning: From One Agent to Many (PDF)
B2: Formula-Based Epistemic Planning
Betreuer:
Dr. Thorsten Engesser
- Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (PDF)
B3: DEL-Based Epistemic Planning
Betreuer:
Dr. Thorsten Engesser
- Epistemic planning for single and multi-agent systems (PDF)
- Don't Plan for the Unexpected: Planning Based on Plausibility Models (PDF)
Area C: Multi-Agent Path Finding (MAPF)
C1: MAPF variations and algorithms
Betreuer:
Prof. Dr. Bernhard Nebel
- AI Buzzwords Explained: Multi-Agent Path Finding (MAPF) (PDF)
- Search-Based Optimal Solvers for the Multi-Agent Pathfinding Problem: Summary and Challenges (PDF)
C2: MAPF reduced to SAT
Betreuer:
Prof. Dr. Bernhard Nebel
- Efficient SAT Approach to Multi-Agent Path Finding under the Sum of Costs Objective (PDF)
C3: Online Multi-Agent Pickup and Delivery via Token Passing
Betreuer:
Rolf-David Bergdoll
- Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks (PDF)
C4: Persistent and Robust Execution of Multi-Agent Pickup and Delivery
Betreuer:
Rolf-David Bergdoll
- Persistent and Robust Execution of MAPF Schedules in Warehouses (PDF)
Area D: Multi-Agent Planning
D1: Privacy Preserving Multi-Agent Planning
Betreuer:
Patrick Caspari
- Privacy Preserving Multi-Agent Planning with Provable Guarantees (PDF)
D2: Multi-Agent Planning as Satisfiability
Betreuer:
Patrick Caspari
- mu-SATPLAN - Multi-agent planning as satisfiability (PDF)
Area E: Probabilistic Planning
E1: Probabilistic Planning via Determinization in Hindsight
Betreuer:
Grigorios Mouratidis
- Probabilistic Planning via Determinization in Hindsight (PDF)
E2: Probabilistic Planning Based on UCT
Betreuer:
Grigorios Mouratidis
- PROST Probabilistic Planning Based on UCT (PDF)
E3: Probabilistic Logic Programming
Betreuer:
Dr. Tim Schulte
- Markov Descision Processes Specified by Probabilistic Logic Programming: Representation and Solution (PDF)
E4: Privacy-preserving Planning in Stochastic Environments
Betreuer:
Dr. Tim Schulte
- Privacy-preserving Planning in Stochastic Environments (PDF)
Area F: Related Problems
F1: Proving (and Certifying) Unsolvability
Betreuer:
Dr. Robert Mattmüller
- Unsolvability Certificates for Classical Planning (PDF)
- A Proof System for Unsolvable Planning Tasks (PDF)
- Certifying Planning Systems: Witnesses for Unsolvability (PDF)
- Tutorial: Certified Unsolvability in Classical Planning (PDF)
F2: Generalized Planning
Betreuer:
Dr. Robert Mattmüller
- A review of generalized planning (PDF)
F3: Action Model Acquisition
Betreuer:
Dr. Robert Mattmüller
- Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary (PDF)
F4: Complexity of h^+ heuristics in HTN Planning
Betreuer:
Dr. Gregor Behnke
- On the Feasibility of Planning Graph Style Heuristics for HTN Planning (PDF)
F5: Hierarchical Planning via Translation to Classical Planning
Betreuer:
Dr. Gregor Behnke
- Translating HTNs to PDDL: A Small Amount of Domain Knowledge Can Go a Long Way (PDF)