Download Research Project Third-party funded project Project title Reasoning

Research Project
Reasoning about Plans and Heuristics for Planning and
Combinatorial Search
Third-party funded project
Project title Reasoning about Plans and Heuristics for Planning and Combinatorial Search
Principal Investigator(s) Helmert, Malte;
Project Members Röger, Gabriele; Pommerening, Florian; Seipp, Jendrik;
Organisation / Research unit Departement Mathematik und Informatik / Artificial Intelligence (Helmert)
Project Website http://ai.cs.unibas.ch/research/
Project start 01.04.2015
Probable end 31.03.2018
Status Active
Action planningis one of the oldest and one of the central research problems in artificial intelligence. A
planning problem arises whenever an intelligent agent (virtual or embodied) needs to decide which activities it
must carry out in order
to achieve its goals, or as Patrik Haslum put it succinctly: "Planning is the art and practice of thinking before
acting."
Classical planning, the most widely studied variant of the planning problem with which this project is
concerned, can be formalized as finding shortest paths (optimal planning) or any path (satisficing planning)
from a giveninitial stateto somegoal statein an implicitly givenplanning task, essentially a directed graph.
Planning is hard because these graphs are often enormous in size in practical applications, comprising
10100states or more, precluding the application of traditional "blind" graph search methods.
The most successful planning algorithms of the last decade fall into the general class of heuristic
searchmethods, usually based on A* or a related search algorithm. In this context, the wordheuristic, which
carries a wide range of meanings in different areas of computer science, is a very specific technical term: a
heuristic is a function whose input is a statesof a planning task and which returns an estimate of the distance
fromsto a nearest goal state. Heuristics are the heart and soul of planning algorithms based on state-space
search. Depending on the accuracy of the heuristic estimates, heuristic search algorithms can range from
solving planning tasks blindingly fast to being worse than blind search.
Hence, much of the most influential classical planning research in the recent past has been concerned with
developing better and better heuristics and furthering our theoretical understanding of existing heuristics. This
has led to increasingly more sophisticated approaches. A large
amount of human ingenuity has gone into devising today's advanced approaches and developing the
underlying theory, especially in the case of optimalplanning, where heuristics must satisfy a lower-bound
property (beadmissible) in order to be safe to use.
The central idea of this project is to shift this burden of sophistication and ingenuity away from the human
heuristic designer and onto the machines that perform the heuristic search. Equipped with appropriate
reasoning algorithms and representations, computers can derive knowledge about planning tasks more
quickly, more reliably and in more complex settings than we humans are capable of. A planning researcher or
practitioner should be able to focus on which informationa planning algorithm ought to exploit, without having
to worry abouthowto exploit it in the best way. In other words, we are suggesting adeclarativeapproach to
heuristic search planning that enables planning algorithms to reason about plans and heuristics in powerful
and general ways.
A key research challenge in this endeavour is identifying an appropriate level of representation at which this
declarative information is specified. It should be generalenough to capture the concepts that today's state-ofthe-art planning algorithms rely on in order to obtain accurate heuristic estimates, yetlimitedenough to afford
efficient reasoning algorithms. In this project, we explore this design space in depth, both theoretically and with
an emphasis on efficient practical algorithms.
Financed by
Swiss National Science Foundation (SNSF)
Follow-up project of
1121982 Abstraction Heuristics for Planning and Combinatorial Search
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