Elicitation in combinatorial auctions with restricted

preferences and bounded interdependency between items

Vincent Conitzer, Tuomas Sandholm, Carnegie Mellon University Paolo Santi, Pisa University

Corresponding to papers:Santi, Conitzer, Sandholm, “Towards a Characterization of Polynomial Preference Elicitation in CAs” (COLT-04)Conitzer, Sandholm, Santi, “Combinatorial Auctions with

k-wise Dependent Valuations” (Draft)

Introduction

Combinatorial auction

• Can bid on combinations of items – Bidder’s perspective:

• Allows bidder to express what she really wants– Avoids exposure problems– No need for lookahead / counterspeculation

– Auctioneer’s perspective: • Automated optimal bundling• Winner determination problem:

– Label bids as winning or losing so as to maximize sum of bid prices

» Each item can be allocated to at most one bid– If approximating, watch incentives

– => Better allocations of items than in noncombinatorial auctions

Another complex problem in combinatorial auctions:

• In direct-revelation mechanisms (e.g. VCG), bidders bid on all 2m combinations– Need to compute the valuation for exponentially many

combinations• Each valuation computation can be NP-complete local planning problem• E.g. carrier company bidding on trucking tasks: TRACONET [Sandholm

AAAI-93] – Need to communicate the bids– Need to reveal the bids => Loss of privacy & strategic info

• Bidding languages [Sandholm 98, 99; Nisan 00; Hoos & Boutilier 01] do not solve the problem

Clearing algorithm

What info is needed from an agent depends on what others have revealed

Elicitor

Conen & Sandholm IJCAI-01 workshop on Econ. Agents, Models & Mechanisms, ACMEC-01

Elicitor decides what to ask next based on answers it has received so far

$ 1,000 for

$ 1,500 for

? for

Related research• Nondeterministic (i.e., oracle) models

– Bikhchandani & Ostroy JET-02– Gul & Stacchetti JET-00– Conitzer & Sandholm AAAI-02– Parkes AMEC-02– Nisan & Segal 03– Segal 04

• Deterministic models– Ascending CAs, e.g. Parkes 99; Wurman & Wellman 00; Ausubel &

Milgrom 02; Kwasnicka, Ledyard, Porter, DeMartini 04– General elicitation framework

• General preferences (no externalities, free disposal) – Conen & Sandholm IJCAI-01 workshop, ACMEC-01, AAAI-02, AMEC-02– Hudson & Sandholm AMEC-02, AAMAS-03, AAMAS-04

• Restricted valuation classes [Techniques from computational learning theory]– Zinkevich, Blum, Sandholm ACMEC-03– Blum, Jackson, Sandholm, Zinkevich COLT-03, JMLR-04– Lahaie & Parkes ACMEC-04

Partial vs. full elicitation• In general, can achieve savings in elicitation by basing queries to one

agent on answers from others

• Here, will assume that auctioneer will want to know each agent’s entire preference function

– So can focus on eliciting one agent’s function

• Will assume that agent’s valuation function is drawn from a restricted class of functions

Model• Set of items I for sale• Bidder has true valuation function v: 2I • Elicitor knows class of functions C with v C• Elicitor’s goal is to identify v• Elicitor can ask bidder for v(B) for any bundle B

– Counts as one (value) query

• Distinguish between eliciting using – polynomial #queries– polynomial time

• May take significant time to compute which query to ask

Some examples of polynomial-query elicitable classes

Read-once valuations [Zinkevich, Blum, Sandholm 03]

Valuations are represented by a tree

Leaf nodes correspond to items and their values

Nonleaf nodes (gates) perform operations including:

SUM: computes the sum of its children

MAXc: computes sum of the c highest inputs

ATLEASTc: returns sum of inputs if at least c nonzero

PLUS

ALL

MAX

ALL

500 400 200 100

1000

150

RO+M: only MAX and SUM allowed

Toolt (=ToolboxDNF)

[Zinkevich, Blum, Sandholm 03]

Valuation represented by polynomial with items as variables

Using only t monomials

A B C D

3 2+ = 5

3A + 5AB + 2AC + 4DAll coefficients must be nonnegative

Can be elicited in O(mt) queries

Tool-t (slight variation)Here, weights on monomials with 2 items must be negative

A B C D

3 = 4

3A + 6B + 2C + D - 2AB - AC

- 1 2+Thrm. Can be elicited in O(mt) queries

Proof: First ask all singletons. Then, discover monomials one by one. Only need to find minimal subset of items that has value less than sum of contained monomials discovered so far. So, start by querying grand bundle and remove items one by one.

Interval bidsItems are ordered on a line

Value of bundle = sum of values of disjoint components

A B C

v({A}) = 1 v({B}) = 2 v({C}) = 2

v({A, B}) = 4

v({B, C}) = 3

v({A, B, C}) = 5

IMPLIED: v({A, C}) = v({A})+v({C}) = 3

Thrm. Can be elicited using m(m+1)/2 queries if ordering is known

Thrm. Can be elicited using m2 – m + 1 queries if ordering is not known, but v({x, y}) > v({x}) + v({y}) iff x and y are adjacent

Proof: Ask all singletons and pairs to find adjacencies (m(m+1)/2), then ask remaining components (m(m-1)/2 – (m-1)), for total of m 2 – m + 1 queries

Tree bids require exponential queries

Natural generalization: tree such that value of bundle = sum of values of disjoint components

Requires exponentially many queries:

……

There are 2m/2 such connected bids

Bounded interdependency

0+1+2 = 3

G2 = 2-wise dependent valuations

1

3

3-2

0

2

1Node = item

Value of bundle = sum of values of nodes/edges in bundle

Gk = k-wise dependent valuations

Value of bundle = sum of values of nodes/edges/multiedges in bundle

For example, k=3:

1

3

3

-20

2

1Node = item

1

3-edge

Gk basic elicitation results

• Thrm. Every valuation function has a unique Gm representation– Proof: Suppose we have found the unique weights for multiedges up to size

j. Then weight of multiedge over S (with |S| = j+1) must be v(S) – S’Sw(S’)

• Thrm. A function in Gk can be elicited in O(mk) queries– Proof: Query all bundles of size k or less. Again, weight of multiedge over S

(with |S| = j+1 k) must be v(S) – S’Sw(S’), so can use dynamic programming

Optimal clearing is still hard in G2

• Pf: reduces from EXACT-COVER-BY-3-SETS1

1

1

1

11

• Can get total value of 2m/3 if and only if an exact 3-cover exists

Special case: union of graphs is forest

• Thrm. Can solve clearing problem to optimality by dynamic programming in time O(mn)

3

6

7

1

1

9

2

Approximating with G2 or Gk

• Thrm. Suppose there exists some v’ in Gk such that for any bundle S, |v(S) – v’(S)| ≤ δ. Then, using O(mk) queries, we can construct a function g in Gk such that for any bundle S, |v(S) – g(S)| ≤ δ(1+(|S| choose k)).

– Bound is tight for G2

• Thrm. Suppose that all the weights in v’s Gm graph are positive. Then, using m(m+1)/2 queries, we can construct a function g in G2 such that for any S, |v(S)-g(S)| ≤ (M(v)/2) ((|S|(|S|+1)/2) (1+ |(|S|-1)/2)

– here M(v) is a measure of the function’s disagreement with the same function without any multiedges

Unions of classes

• Let C1, C2 be valuation classes that can be elicited with polynomial #queries

– Using algorithms A1, A2 with query bounds p1(n), p2(n)

• Consider the following simple algorithm for C1 C2

1. f1A1 , f2A2

2. If f1 = f2, return it

3. Otherwise, find bundle S such that f1(S) f2(S)

4. Query v(S)

5. If f1(S) = v(S), return f1, otherwise f2

• At most p1(n) + p2(n) + 1 queries

• Gives no bound on computation: checking identity of functions in steps 2, 3 may take lot of computation

Polynomial-query elicitable valuation classes closed under pairwise union

• Consider the following classes:

• C1 = {fs} where

– fs(B) = 0 if B is empty or B = {s}

– fs(B) = 2 if B = I

– fs(B) = 1 otherwise

p1(n) + p2(n) + 1 bound is tight

• To elicit C1, simply ask v({s}) for every s– Need at most m-1 queries

• C2 = {f-s} where – f-s(B) = 0 if B is empty– f-s(B) = 2 if B = I or B = I – {s}– f-s(B) = 1 otherwise

• To elicit C2, simply ask v(I-{s}) for every s– Need at most m-1 queries

• To elicit C1 C2 , need to find {s} or I-{s} with value different from 1– Need 2m-1 = 2(m-1) + 1 queries

{}

{a} {b} {c}

0

1 0 1

I

I-{a} I-{b} I-{c}

1 2 1

2

Does taking the union ever make computation harder?

• Answer: yes. Consider following class:

• G2U: valuation is given by graph from G2 (with

positive edge weights) + upper bound u on value

1

3 3

2

2

A

B C

u = 6 v({A, C}) = 6

• Easy to elicit: – ask all singletons, all pairs to get graph– ask grand bundle to get u

Taking the union may make computation harder…

• Now consider the following class:• G2

UH: same as G2U except no more than half of bundle’s value can come from edges

– require: no edge worth more than sum of endpoints

1

3 3

2

2

A

B C

u = 20 v({A, B, C}) = 10

• Again, easy to elicit: – ask all singletons, all pairs to get graph– ask grand bundle to get u

How computationally hard is it to elicit G2

U G2UH?

• Thrm. It is coNP-complete to determine whether a function from G2

U and another from G2UH

(represented by their graphs and u) are identical– That is, it is NP-complete to find a bundle whose

query would distinguish them

Proof of hardness• Reduction from CLIQUE problem

every vertex: weight 1

Required clique size:k (say, 3)

every edge: weight(k+) / (k choose 2)

u = 2k +

• Clique of size k would have k vertex weight and k+ edge weight– So, G2

UH at-most-half-from-edges constraint would be binding

• Cannot happen when there are fewer edges

• For larger sets, the u-constraint is binding

Optimized polynomial-time elicitation algorithm for RO+M Tool-t Toolt G2 INT

Thrm. Runs in polynomial time and uses at most O(m(m+t)) queries

Towards characterizing easily elicitable valuation functions

Polynomial inferability• Inferring a bundle = ascertaining its value from queries on other

bundles• Bundle is polynomially noninferable (strongly polynomially

noninferable) wrt C if for some (any) function in C, the bundle’s valuation cannot be inferred using polynomially many queries

• Thrm. There exists a class of functions where– exponentially many bundles are polynomially noninferable– no bundles are strongly polynomially noninferable– the class cannot be elicited using polynomially many queries.

• Proof uses [Angluin 88] idea, functions of the form:

…v(B) = 1 iff B contains all items corresponding to a

color

Conclusions• Focused on learning full valuation function in restricted classes• New easy-to-elicit classes of valuations

– Tool-t, Interval, Gk

• Clearing for G2 is NP-complete– But easy if union of graphs is forest

• Approximation with functions from G2 or Gk

• Polyquery elicitable classes closed under pairwise union– But computation required may go from polynomial to NP-hard– Efficient algorithm for union of most of the classes studied

• Even classes without strongly polynomially noninferable bundles may require exponentially many queries for elicitation

Future research

• Can Interval class be elicited with polynomially many queries without knowing the order?

• Can we come up with a more general characterization of what makes valuation functions easy to elicit?

• What if we have a restricted class of valuations and we only need to elicit enough to allocate (or compute VCG payments)?

Thank you for your attention!