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Beyond Distance Estimates: A New Theory of Heuristics for State-Space Search

Project description

A new estimator model for state space search

State space search is considered a central research problem in artificial intelligence since day one. Algorithms in the A* family still represent the strategic method for state space research. A* is a graph search algorithm whose ‘heuristic function’ estimates the distance from a state to the nearest targeted state. The efficiency of A* relies on the precision of this estimate. The EU-funded BDE project will inquire into the A* + distance estimator model and explore three new ways that overdraw the classical method. The project will propose a new model of declarative heuristics and advise moving the burden of creativity away from the human expert. BDE will recommend to abandon the idea of exploring sequential trails in state spaces and transform state space research into combinatorial optimisation problems with no defined sequencing aspect.

Objective

"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."

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Programme(s)

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Topic(s)

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Funding Scheme

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ERC-COG - Consolidator Grant

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Call for proposal

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(opens in new window) ERC-2018-COG

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Host institution

UNIVERSITAT BASEL
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 997 510,00
Address
PETERSPLATZ 1
4051 Basel
Switzerland

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Region
Schweiz/Suisse/Svizzera Nordwestschweiz Basel-Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 997 510,00

Beneficiaries (1)

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