Methods and systems for multi-participant interactive evolutionary computing

• US 7,624,077 B2
• Filed: 08/28/2007
• Issued: 11/24/2009
• Est. Priority Date: 08/27/2003
• Status: Active Grant

• Current Assignee: Telepathy Labs, Inc.
• Original Assignee: Icosystem Corp.
• Inventors: Funes, Pablo, Bonabeau, Eric
• Primary Examiner(s): Holmes; Michael B
• Application Number:
  US11/846,267
• Publication Number:
  US 20080021855A1
• Time in Patent Office: 819 Days
• Field of Search: 706/13
• US Class Current: 706/13
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

Disclosed are methods, systems, and processor program products that include executing an optimization scheme to obtain a first solution set, presenting the first solution set to at least two users, receiving rankings of the first solution set from the at least two users, aggregating the rankings, and, generating a second solution set based on the aggregated rankings. The optimization scheme can include a genetic algorithm. In embodiments, at least a part of the first solution set can be presented to the users based on the parts of the solution set associated with the user (e.g., user'"'"'s knowledge).

Claims

1. In a computer system having at least one user interface including at least one output device and at least one input device, a method of scheduling work assignments, comprising:

a) causing at least a part of a solution set of work assignments to be presented through at least one of the at least one output devices to at least two first-level users,

b) receiving through at least one of the at least one input devices, from at least one of the at least two first-level users, a ranking of the at least a part of the solution set of work assignments presented to that user,

c) if rankings of at least a part of the solution set were received from a plurality of first-level users, aggregating the rankings and generating an updated solution set of work assignments based on the aggregated rankings,

d) if rankings of at least a part the solution set were received from only one first-level user, generating an updated solution set of work assignments based on the ranking by the said user,

e) causing the updated solution set of work assignments to be presented through at least one of the at least one output devices to at least one second-level user,

f) receiving through at least one of the at least one input devices, from at least one of the at least one second-level users, a ranking of the updated solution set of work assignments,

g) if rankings of the updated solution set were received from a plurality of second-level users, aggregating the rankings, and generating a further updated solution set of work assignments based on the aggregated rankings,

h) if rankings of the updated solution set were received from only one second-level user, generating a further updated solution set of work assignments based on the ranking by the said user,

i) determining if a stopping criterion has been satisfied,

j) if the said stopping criterion has not been satisfied, repeating steps a) through i) based upon the further updated solution set of work assignments, and

k) if the said stopping criterion has been satisfied, causing at least a portion of the further updated solution set of work assignments to be presented to at least one user through at least one of the at least one output devices.

2. A method according to claim 1, where generating an updated solution set includes generating the updated solution set using an optimization scheme.

3. A method according to claim 2, where the optimization scheme comprises a genetic algorithm.

4. A method according to claim 3, where the genetic algorithm is a single generation genetic algorithm.

5. A method according to claim 3, where the genetic algorithm is a multiple generation genetic algorithm.

6. A method according to claim 2, further comprising determining whether at least one exit criterion is satisfied before ceasing to execute the optimization scheme.

7. A method according to claim 2, further comprising adjusting at least a part of the optimization scheme based upon input from at least one second-level user.

8. A method according to claim 7, where adjusting at least part of the optimization scheme includes modifying parameters of the optimization scheme.

9. A method according to claim 8, where modifying parameters of the optimization scheme includes modifying parameters of a genetic algorithm.

10. A method according to claim 1, where causing at least a part of a solution set to be presented through at least one of the at least one output devices includes:

determining, for each first-level user, which at least a part of the solution set is associated with the said user, and,

causing to be presented to each user through at least one of the at least one output devices the at least a part of the first solution set associated with the said user.

11. A method according to claim 10, where determining which at least a part of the solution set is associated with the said user includes determining based on at least one of a knowledge of the said user, an involvement of the said user, and an experience level of the said user.

12. A method according to claim 1, where causing at least a part of a solution set to be presented through at least one of the at least one output devices includes causing to be presented at least one of:

in parallel and in series.

13. A method according to claim 1, where receiving rankings through at least one of the at least one input devices includes receiving rankings within a time period.

14. A method according to claim 13, where receiving rankings within a time period includes:

determining that a time period expired, and,

assigning a ranking based on a prior ranking received from a user.

15. A method according to claim 13, where receiving rankings within a time period includes:

determining that a time period expired, and,

assigning a ranking based on a default ranking.

16. A method according to claim 1, where aggregating the rankings includes aggregating based on weightings assigned to the plurality of users from whom the rankings were received.