goods are sold from a centralized stock each agent starts with a random endowment of entertainment tickets  The prices sent to agents are the bid-ask spreads ie  the highest current bid price and the lowest current ask price  due to immediate clears  ask price is always greater than bid price When a bid that beats the current bid ask price arrives the sale price is the standing bid ask price as opposed to the arriving ask  bid  price  In this case  bid withdrawal and ticket resale are both permitted In addition to unpredictable market prices  other sources of variability from game in stance to game instance are the client profiles assigned to the agents and the random initial allotment of entertainment tickets Each TAC agent has clients with randomly assigned travel preferences Clients have parameters for ideal arrival day IAD
ideal depar ture day IDD
grand hotel value  GHV  $    $
and entertainment values  EV
for each type of entertainment ticket e The utility obtained by a client is determined by the travel package that it is given in combination with its preferences  To obtain a non zero utility  the client must be assigned a feasible travel package consisting of an arrival day AD with the corresponding inflight departure day  DD with the corresponding outflight  and hotel rooms of the same type   BGH
or LFI  for each day   such that At most one entertainment ticket can VI
 and no client can be given more than one of the be assigned for each day V
same entertainment ticket type  Given a feasible package  the client s utility is defined as
- travelPenalty   hotelBonus   funBonus where
- travelPenalty

$$\begin{array}{r l r l r l r l r l}{{}}&{{}}&{{}}&{{}}&{{}}&{{}}&{{}}\\ {{}}&{{}}&{{}}&{{}}&{{}}&{{}}&{{}}&{{}}\\ {{}}&{{}}&{{}}&{{}}&{{}}&{{}}&{{}}&{{}}\end{array}$$
 $GHV\:\text{if the client is in the GBH}\quad\text{otherwise}$ . 
- hotelBonus sum of relevant EV s for each entertainment ticket type assigned to the
- funBonus client
A TAC agent s final score is simply the sum of its clients utilities minus the agent s expenditures  Throughout the game instance  it must decide what bids to place in each of auctions  At the end of the game  it must submit a final allocation of purchased t he goods to its clients The client preferences  allocations  and resulting utilities from one particular game from on the TAC server  are shown in Tables   and the TAC finals  Game For full details on the design and mechanisms of the TAC server see Wellman et al

## 3. Attac-2000

ATTac-200 finished first in the Trading Agent Competition using a principled bidding strategy  which included several elements of adaptivity   This adaptivity gave ATTac-2000 the flexibility to cope with a wide variety of possible scenarios at the competition In this ssection we describe ATTac-2000s bidding strategy its method for determining the best allocation of goods to clients  and its three forms of adaptivity  ATTac-2000 s high level strategy is summarized in Table