
Comments on both
approaches are welcome.
Applying a BDI Agent Architecture to
the Swan Management DSS:
Regarding the swan management
DSS, the following agents might be considered:
Beliefs:
|
Belief
Revision Function:
would run breeding
habitat and migration expert systems again as needed; would access trends
[Should this be three
agents (or at least three brf's) --trends, breeding habitat quality, and
migration?]
|
Options:
|
Option
Generation Function:
would run montane wetland management expert system as needed [May not require all intentions to do this. Is this a flaw in applying bdi logic?] |
Intentions:
meet wildlife objective and associated habitat type (from montane wetland management expert system) |
Filter:
calls RMP Agent to determine intentions on numbers. [This is external to the Refuge Specific Agent. Is this a flaw in applying bdi logic?] Running the ogf would also ensure that wildlife/ habitat intentions are met. |
Actions:
|
Action
Selection Function:
??? [Write an algorithm to determine if revised options are attainable/ realistic and if they will contribute to reaching revised intentions.] |
Belief Revision Function: would run breeding habitat and migration expert systems again as needed; would access trends DB/KB
[Should this be three agents (or at least three brf's) --trends, breeding habitat quality, and migration?]
RMP Agent
Beliefs:
|
Belief
Revision Function:
would access trends DB/KB; would query user |
Options:
Fall migration areas?[?] |
Option
Generation Function:
The set of options is static. [Write an algorithm for eliminating any options obviously infeasible for individual refuge/ time.???] |
Intentions:
local area should or should not try to contribute to RMP wintering numbers and distribution |
Filter:
would run flyway management expert system |
Actions:
|
Action
Selection Function:
[Write algorithm with rules to prioritize seasonal goals.] |