Summary
"Many problems in computer science can be cast as state-space search, where the
objective is to find a path from an initial state to a goal state in a
directed graph called a ""state space"". State-space search is challenging due
to the state explosion problem a.k.a. ""curse of dimensionality"": interesting
state spaces are often astronomically large, defying brute-force exploration.
State-space search has been a core research problem in Artificial Intelligence
since its early days and is alive as ever. Every year, a substantial fraction
of research published at the ICAPS and SoCS conferences is concerned with
state-space search, and the topic is very active at general AI conferences
such as IJCAI and AAAI.
Algorithms in the A* family, dating back to 1968, are still the go-to approach
for state-space search. A* is a graph search algorithm whose only
""intelligence"" stems from a so-called ""heuristic function"", which estimates
the distance from a state to the nearest goal state. The efficiency of A*
depends on the accuracy of this estimate, and decades of research have pushed
the envelope in devising increasingly accurate estimates.
In this project, we question the ""A* + distance estimator"" paradigm and
explore three new directions that go beyond the classical approach:
1. We propose a new paradigm of declarative heuristics, where heuristic
information is not represented as distance estimates, but as properties of
solutions amenable to introspection and general reasoning.
2. We suggest moving the burden of creativity away from the human expert by
casting heuristic design as a meta-optimization problem that can be solved
automatically.
3. We propose abandoning the idea of exploring sequential paths in state
spaces, instead transforming state-space search into combinatorial
optimization problems with no explicit sequencing aspect. We argue that the
""curse of sequentiality"" is as bad as the curse of dimensionality and must
be addressed head-on."
objective is to find a path from an initial state to a goal state in a
directed graph called a ""state space"". State-space search is challenging due
to the state explosion problem a.k.a. ""curse of dimensionality"": interesting
state spaces are often astronomically large, defying brute-force exploration.
State-space search has been a core research problem in Artificial Intelligence
since its early days and is alive as ever. Every year, a substantial fraction
of research published at the ICAPS and SoCS conferences is concerned with
state-space search, and the topic is very active at general AI conferences
such as IJCAI and AAAI.
Algorithms in the A* family, dating back to 1968, are still the go-to approach
for state-space search. A* is a graph search algorithm whose only
""intelligence"" stems from a so-called ""heuristic function"", which estimates
the distance from a state to the nearest goal state. The efficiency of A*
depends on the accuracy of this estimate, and decades of research have pushed
the envelope in devising increasingly accurate estimates.
In this project, we question the ""A* + distance estimator"" paradigm and
explore three new directions that go beyond the classical approach:
1. We propose a new paradigm of declarative heuristics, where heuristic
information is not represented as distance estimates, but as properties of
solutions amenable to introspection and general reasoning.
2. We suggest moving the burden of creativity away from the human expert by
casting heuristic design as a meta-optimization problem that can be solved
automatically.
3. We propose abandoning the idea of exploring sequential paths in state
spaces, instead transforming state-space search into combinatorial
optimization problems with no explicit sequencing aspect. We argue that the
""curse of sequentiality"" is as bad as the curse of dimensionality and must
be addressed head-on."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/817639 |
| Start date: | 01-02-2019 |
| End date: | 31-01-2024 |
| Total budget - Public funding: | 1 997 510,00 Euro - 1 997 510,00 Euro |
Cordis data
Original description
"Many problems in computer science can be cast as state-space search, where theobjective is to find a path from an initial state to a goal state in a
directed graph called a ""state space"". State-space search is challenging due
to the state explosion problem a.k.a. ""curse of dimensionality"": interesting
state spaces are often astronomically large, defying brute-force exploration.
State-space search has been a core research problem in Artificial Intelligence
since its early days and is alive as ever. Every year, a substantial fraction
of research published at the ICAPS and SoCS conferences is concerned with
state-space search, and the topic is very active at general AI conferences
such as IJCAI and AAAI.
Algorithms in the A* family, dating back to 1968, are still the go-to approach
for state-space search. A* is a graph search algorithm whose only
""intelligence"" stems from a so-called ""heuristic function"", which estimates
the distance from a state to the nearest goal state. The efficiency of A*
depends on the accuracy of this estimate, and decades of research have pushed
the envelope in devising increasingly accurate estimates.
In this project, we question the ""A* + distance estimator"" paradigm and
explore three new directions that go beyond the classical approach:
1. We propose a new paradigm of declarative heuristics, where heuristic
information is not represented as distance estimates, but as properties of
solutions amenable to introspection and general reasoning.
2. We suggest moving the burden of creativity away from the human expert by
casting heuristic design as a meta-optimization problem that can be solved
automatically.
3. We propose abandoning the idea of exploring sequential paths in state
spaces, instead transforming state-space search into combinatorial
optimization problems with no explicit sequencing aspect. We argue that the
""curse of sequentiality"" is as bad as the curse of dimensionality and must
be addressed head-on."
Status
CLOSEDCall topic
ERC-2018-COGUpdate Date
27-04-2024
Geographical location(s)
