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| Submission declined on 24 March 2024 by
KylieTastic (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
This draft has been resubmitted and is currently awaiting re-review. |  |
| Submission declined on 17 March 2024 by
KylieTastic (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
| |
Feature Space Search (FESS) is a novel single-agent search algorithm designed to address limitations of traditional search methods in complex domains. Unlike other search algorithms that use features and combine them into a single value, FESS treats the features as an abstract space (feature space) to be explored. It guides the search process in the domain space through exploration within this feature space, without relying on an evaluation or heuristic function.
Generality
FESS is designed as a general approach, offering a potentially more efficient solution for various problems by exploring features independently and providing multi-objective guidance to the search process. This makes it well-suited for challenging problems where finding a solution is a priority, even if not the most optimal one. While initially applied to Sokoban puzzles, its flexibility allows for adaptation to other domains with complex problem-solving requirements.
Development
Motivated by the need for better search strategies, the algorithm was presented at the 2020 IEEE Conference on Games (CoG) by Yaron Shoham and Prof. Jonathan Schaeffer. [1]
Performance
Empirical evaluations have shown strong performance compared to other search methods in Sokoban puzzles. [2]
FESS Process
FESS works by discovering new cells in feature-space until a path is established between the start cell and the target cell. This is done by iterating over the cells and attempting to find unvisited neighbours. Moves are played in the domain space with the aim of discovering new feature-space cells.
External links
References
- ^ Yaron Shoham; Jonathan Shaeffer (2020). The FESS Algorithm: A Feature Based Approach to Single-Agent Search (PDF). 2020 IEEE Conference on Games (CoG). Osaka, Japan: IEEE. doi: 10.1109/CoG47356.2020.9231929.
- ^ Christopher Olson; Lars Wagner; Alexander Dockhorn (2023). Evolutionary Optimization of Baba Is You Agents (PDF). 2023 IEEE Congress on Evolutionary Computation (CEC). Chicago, USA: IEEE. p. 3. doi: 10.1109/CEC53210.2023.10253977.
| Review waiting, please be patient.
This may take 2 months or more, since drafts are reviewed in no specific order. There are 2,355 pending submissions waiting for review.
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How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
Reviewer tools
|
| Submission declined on 24 March 2024 by
KylieTastic (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
This draft has been resubmitted and is currently awaiting re-review. | |
| Submission declined on 17 March 2024 by
KylieTastic (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
| |
Feature Space Search (FESS) is a novel single-agent search algorithm designed to address limitations of traditional search methods in complex domains. Unlike other search algorithms that use features and combine them into a single value, FESS treats the features as an abstract space (feature space) to be explored. It guides the search process in the domain space through exploration within this feature space, without relying on an evaluation or heuristic function.
Generality
FESS is designed as a general approach, offering a potentially more efficient solution for various problems by exploring features independently and providing multi-objective guidance to the search process. This makes it well-suited for challenging problems where finding a solution is a priority, even if not the most optimal one. While initially applied to Sokoban puzzles, its flexibility allows for adaptation to other domains with complex problem-solving requirements.
Development
Motivated by the need for better search strategies, the algorithm was presented at the 2020 IEEE Conference on Games (CoG) by Yaron Shoham and Prof. Jonathan Schaeffer. [1]
Performance
Empirical evaluations have shown strong performance compared to other search methods in Sokoban puzzles. [2]
FESS Process
FESS works by discovering new cells in feature-space until a path is established between the start cell and the target cell. This is done by iterating over the cells and attempting to find unvisited neighbours. Moves are played in the domain space with the aim of discovering new feature-space cells.
External links
References
- ^ Yaron Shoham; Jonathan Shaeffer (2020). The FESS Algorithm: A Feature Based Approach to Single-Agent Search (PDF). 2020 IEEE Conference on Games (CoG). Osaka, Japan: IEEE. doi: 10.1109/CoG47356.2020.9231929.
- ^ Christopher Olson; Lars Wagner; Alexander Dockhorn (2023). Evolutionary Optimization of Baba Is You Agents (PDF). 2023 IEEE Congress on Evolutionary Computation (CEC). Chicago, USA: IEEE. p. 3. doi: 10.1109/CEC53210.2023.10253977.