### Abstract

Planning is defined as the problem of synthesizing a desired behavior from given basic operations, and learning is defined as the dual problem of analyzing a given behavior to determine the unknown basic operations. Algorithms for solving these problems in the context of invertible operations on finite-state environments are developed. In addition to their obvious artificial intelligence applications, the algorithms can efficiently find the shortest way to solve Rubik's cube, test ping-pong protocols, and solve systems of equations over permutation groups.

Original language | English |
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Title of host publication | Annual Symposium on Foundations of Computer Science (Proceedings) |

Publisher | Publ by IEEE |

Pages | 274-279 |

Number of pages | 6 |

ISBN (Print) | 0818619821 |

Publication status | Published - Nov 1989 |

Event | 30th Annual Symposium on Foundations of Computer Science - Research Triangle Park, NC, USA Duration: Oct 30 1989 → Nov 1 1989 |

### Other

Other | 30th Annual Symposium on Foundations of Computer Science |
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City | Research Triangle Park, NC, USA |

Period | 10/30/89 → 11/1/89 |

### Fingerprint

### ASJC Scopus subject areas

- Hardware and Architecture

### Cite this

*Annual Symposium on Foundations of Computer Science (Proceedings)*(pp. 274-279). Publ by IEEE.

**Planning and learning in permutation groups.** / Fiat, Amos; Moses, Shahar; Shamir, Adi; Shimshoni, Ilan; Tardos, G.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Annual Symposium on Foundations of Computer Science (Proceedings).*Publ by IEEE, pp. 274-279, 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, NC, USA, 10/30/89.

}

TY - GEN

T1 - Planning and learning in permutation groups

AU - Fiat, Amos

AU - Moses, Shahar

AU - Shamir, Adi

AU - Shimshoni, Ilan

AU - Tardos, G.

PY - 1989/11

Y1 - 1989/11

N2 - Planning is defined as the problem of synthesizing a desired behavior from given basic operations, and learning is defined as the dual problem of analyzing a given behavior to determine the unknown basic operations. Algorithms for solving these problems in the context of invertible operations on finite-state environments are developed. In addition to their obvious artificial intelligence applications, the algorithms can efficiently find the shortest way to solve Rubik's cube, test ping-pong protocols, and solve systems of equations over permutation groups.

AB - Planning is defined as the problem of synthesizing a desired behavior from given basic operations, and learning is defined as the dual problem of analyzing a given behavior to determine the unknown basic operations. Algorithms for solving these problems in the context of invertible operations on finite-state environments are developed. In addition to their obvious artificial intelligence applications, the algorithms can efficiently find the shortest way to solve Rubik's cube, test ping-pong protocols, and solve systems of equations over permutation groups.

UR - http://www.scopus.com/inward/record.url?scp=0024766726&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024766726&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0818619821

SP - 274

EP - 279

BT - Annual Symposium on Foundations of Computer Science (Proceedings)

PB - Publ by IEEE

ER -