OutlineOutline

• Brief description of the GTAAP system

• Review ERA Algorithm

• Adaptations/Changes from Basic ERA Implementation – Optimization

• Demo/Results

• Future Research and Conclusions

OutlineOutline

• Brief description of the GTAAP system

• Review ERA Algorithm

• Adaptations/Changes from Basic ERA Implementation – Optimization

• Demo/Results

• Future Research and Conclusions

ERA: Environment, Rules, Agents [Liu et al, AIJ 02]ERA: Environment, Rules, Agents [Liu et al, AIJ 02]

• Environment is an nxa board • Each variable is an agent • Each position on board is a value of a domain• Agent moves in a row on board• Each position records the number of violations

caused by the fact that agents are occupying other positions

• Agents try to occupy positions where no constraints are broken (zero position)

• Agents move according to reactive rules

Reactive rules [Liu et al, AIJ 02]Reactive rules [Liu et al, AIJ 02]

Reactive rules:– Least-move: choose a position with the min. violation value

– Better-move: choose a position with a smaller violation value

– Random-move: randomly choose a positionCombinations of these basic rules form different behaviors.

R e a ct iv e ru le s B e h a v io rde s ig n e r

LR le a s t-m o v e with 1 -p and ra n d o m -m o v e with p

BR b e tte r -m o v e with 1 -p and ra n d o m -m o v e with p

BLR f i r s t b e tte r -m o v e , i f f ai l the n apply LR

rBLR f i r s t apply b e tte r -m o v e r t im e s , i f f ai l the n apply LR

F rBLR apply rBLR in the f i r s t r i te r at io ns , the n apply LR

Big pictureBig picture

• Agents do not communicate but share a common context

• Agents keep kicking each other out of their comfortable positions until every one is happy

• Charecterization: [Hui Zou, 2003]

– Amazingly effective in solving very tight but solvable instances

– Unstable in over-constrained cases• Agents keep kicking each other out (livelock)• Livelocks may be exploited to identify bottlenecks

OutlineOutline

• Brief description of the GTAAP system

• Review ERA Algorithm

• Adaptations/Changes from Basic ERA Implementation – Optimization

• Demo/Results

• Future Research and Conclusions

Implementation DetailsImplementation Details

• Use of the rBLR as the main behavior (random-move as a supporting behavior)

• Random move’s probability is that it occurs about 2% of the time, the remaining 98% of the time is rBLR.

• “r” in rBLR is set as 3

• Termination: 150 time steps

Additions Made to the Basic AlgorithmAdditions Made to the Basic Algorithm

• Optimization:– agent’s assigned TA’s preference for this class

– Each agent assumes a better move to be:• If the new position has less constraint violations as the old

one.• If the new position has the same number of constraint

violations, but the position’s GTA has a higher preference ranking for this course than the current position’s GTA.

• This provided much, much improved results in practice, by forcing more movement and overall better values to be selected.

Agentsagent

OutlineOutline

• Brief description of the GTAAP system

• Review ERA Algorithm

• Adaptations/Changes from Basic ERA Implementation – Optimization

• Demo/Results

• Future Research and Conclusions

ResultsResults

• Fall 2007

ERA Algorithm Actual Assignment

Quality/Utility Percentage 0.67 0.66

Course Loads Remaining Unassigned 0.00 3.48

ResultsResults

• Spring 2007

ERA Algorithm Actual Assignment

Quality/Utility Percentage 0.65 0.62

Course Loads Remaining Unassigned 0.33 4.16

ResultsResults

• Fall 2004

ERA Algorithm Actual Assignment

Quality/Utility Percentage 0.57 0.58

Course Loads Remaining Unassigned 0.75 2.00

Future ResearchFuture Research

• Testing upcoming semesters to see how well this aids the assignment process in the real-world.

• Setting up courses that are a low priority to be able to remain unassigned.

• Looking into other local search techniques (genetic algorithms, etc.)

• Creation of hybrids of local searches• Investigations in mimicking the human process

(greedy, yet still making a few “back changes”)

ConclusionsConclusions

• As the testing confirmed, this approach seems like it will be a great aid in the assignment process.

• Its results are statistically approximately equal to or better than the human-generated solution, though this still needs to be confirmed in the real-world.

• This approach seems a very good way to go in situations where a decent solution is needed in a relatively small amount of time.