### Abstract

The process of forming coalitions of software agents generally requires calculating a value for every possible coalition which indicates how beneficial that coalition would be if it was formed. Now, since the number of possible coalitions increases exponentially with the number of agents involved, having one agent calculate all the values is inefficient. Given this, we present a novel algorithm for distributing this calculation among agents in cooperative environments. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents' shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, and has minimal memory requirements. To evaluate the effectiveness of our algorithm we compare it with the only other algorithm available in the literature (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took 0.00037% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 477826101 to 0, and the total number of bytes sent between the agents dropped from 674047872 to 0 (note that for larger numbers of agents, these improvements become exponentially better).

Original language | English (US) |
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Pages | 152-157 |

Number of pages | 6 |

State | Published - Dec 1 2005 |

Event | 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, United States Duration: Jul 9 2005 → Jul 13 2005 |

### Other

Other | 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 |
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Country | United States |

City | Pittsburgh, PA |

Period | 7/9/05 → 7/13/05 |

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### ASJC Scopus subject areas

- Software

### Cite this

*Distributing coalitional value calculations among cooperative agents*. 152-157. Paper presented at 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05, Pittsburgh, PA, United States.

**Distributing coalitional value calculations among cooperative agents.** / Rahwan, Talal; Jennings, Nicholas R.

Research output: Contribution to conference › Paper

}

TY - CONF

T1 - Distributing coalitional value calculations among cooperative agents

AU - Rahwan, Talal

AU - Jennings, Nicholas R.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - The process of forming coalitions of software agents generally requires calculating a value for every possible coalition which indicates how beneficial that coalition would be if it was formed. Now, since the number of possible coalitions increases exponentially with the number of agents involved, having one agent calculate all the values is inefficient. Given this, we present a novel algorithm for distributing this calculation among agents in cooperative environments. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents' shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, and has minimal memory requirements. To evaluate the effectiveness of our algorithm we compare it with the only other algorithm available in the literature (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took 0.00037% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 477826101 to 0, and the total number of bytes sent between the agents dropped from 674047872 to 0 (note that for larger numbers of agents, these improvements become exponentially better).

AB - The process of forming coalitions of software agents generally requires calculating a value for every possible coalition which indicates how beneficial that coalition would be if it was formed. Now, since the number of possible coalitions increases exponentially with the number of agents involved, having one agent calculate all the values is inefficient. Given this, we present a novel algorithm for distributing this calculation among agents in cooperative environments. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents' shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, and has minimal memory requirements. To evaluate the effectiveness of our algorithm we compare it with the only other algorithm available in the literature (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took 0.00037% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 477826101 to 0, and the total number of bytes sent between the agents dropped from 674047872 to 0 (note that for larger numbers of agents, these improvements become exponentially better).

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M3 - Paper

SP - 152

EP - 157

ER -