### Abstract

In this paper, we present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains ', and employ the 0-approximation (Son & Baral 2001) to define the regression function. In binary domains, the use of 0-approximation means using 3-valued states. Although planning using this approach is incomplete with respect to the full semantics, we adopt it to have a lower complexity. We prove the soundness and completeness of our regression formulation with respect to the definition of progression and develop a conditional planner that utilizes our regression function.

Original language | English (US) |
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Title of host publication | Proceedings of the National Conference on Artificial Intelligence |

Pages | 556-561 |

Number of pages | 6 |

State | Published - 2004 |

Event | Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) - San Jose, CA, United States Duration: Jul 25 2004 → Jul 29 2004 |

### Other

Other | Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) |
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Country | United States |

City | San Jose, CA |

Period | 7/25/04 → 7/29/04 |

### Fingerprint

### ASJC Scopus subject areas

- Software

### Cite this

*Proceedings of the National Conference on Artificial Intelligence*(pp. 556-561)

**Regression with respect to sensing actions and partial states.** / Tuan, Le Chi; Baral, Chitta; Zhang, Xin; Son, Tran Cao.

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

*Proceedings of the National Conference on Artificial Intelligence.*pp. 556-561, Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004), San Jose, CA, United States, 7/25/04.

}

TY - GEN

T1 - Regression with respect to sensing actions and partial states

AU - Tuan, Le Chi

AU - Baral, Chitta

AU - Zhang, Xin

AU - Son, Tran Cao

PY - 2004

Y1 - 2004

N2 - In this paper, we present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains ', and employ the 0-approximation (Son & Baral 2001) to define the regression function. In binary domains, the use of 0-approximation means using 3-valued states. Although planning using this approach is incomplete with respect to the full semantics, we adopt it to have a lower complexity. We prove the soundness and completeness of our regression formulation with respect to the definition of progression and develop a conditional planner that utilizes our regression function.

AB - In this paper, we present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains ', and employ the 0-approximation (Son & Baral 2001) to define the regression function. In binary domains, the use of 0-approximation means using 3-valued states. Although planning using this approach is incomplete with respect to the full semantics, we adopt it to have a lower complexity. We prove the soundness and completeness of our regression formulation with respect to the definition of progression and develop a conditional planner that utilizes our regression function.

UR - http://www.scopus.com/inward/record.url?scp=9444290409&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=9444290409&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:9444290409

SP - 556

EP - 561

BT - Proceedings of the National Conference on Artificial Intelligence

ER -