TAC Classic: VegBot

Team Members

Venkata Yellapantula

Evan Liu

George Alexander

TAC Classic

● Flight tickets: continuously clearing, infinite supply

● Hotel tickets: 1 random closure every minute

● Event tickets: continuous double auctions

● MAS aspects:– Stochastic, dynamic

environment– Self-interested

agents– Non-local effects

(other agents affect the environment of our agent)

Problem Dimensions

● Price prediction : what will the price of item X be at time T

● Item valuation : what is item X worth to me?● When should I place my bids?● Should I counterspeculate about other

agents?

Agent Design Ideas

● Learning based on historical TAC data (e.g. ATTac)

● Alternate bidding strategies based on assessment of competition (e.g. Risk-averseness)

● Ignore other agents, bid based on personal valuation of goods

● Simple bidding heuristics

Evaluation – 2 Possibilities

● Alternative A– Connect to SICS TAC Classic server– Run lots of games– Evaluate our agent’s average performance

● Alternative B– Download TAC Classic server + some agents

from agent repository– Run many games locally using the same set of

competitors– Evaluate our agent’s performance

Project Timeline

● Research/design: 2/21 - 3/21● Implementation/debugging: 3/21 - 4/11● Experimentation: 4/11 - 4/25● Analysis: 4/25 - 5/2● Presentation of Results: 5/9

References

● TAC Classic website:– http://www.sics.se/tac/page.php?id=3

● Amy Greenwald’s website:– http://www.cs.brown.edu/~amygreen/