automated negotiation lecture 1: introduction and background knowledge
TRANSCRIPT
Introduction: Motivation
Agents search & make contracts
-- Through peer-to-peer negotiation or a mediated marketplace.-- Agents can be real-world parties or software agents that work on behalf of real-world parties.
Increasingly important from a practical perspective
-- Developing communication infrastructure (Internet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Aglets, AgentTCL, Java Applets, ...)-- Electronic commerce on the Internet: Goods, services, information, bandwidth, computation, storage...-- Industrial trend toward virtual enterprises & outsourcing-- Automated negotiation allows dynamically formed alliances on a per order basis in order to capitalize on economies of scale, and allow the parties to stay separate when there are diseconomies of scale
Introduction: Motivation
Fertile, timely research area
-- Deep theories from game-theory & CS merge. Started together in the 1940’s [Morgenstern & von Neumann]. There were a few decades of little interplay. Upswing of interplay in the last few years.-- The intersection is a very fruitful, relatively open research area.
It is in this setting that the prescriptive power of game theory really comes into play.
-- Market rules need to be explicitly specified-- Software agents designed so as to act optimally-- Computational capabilities can be quantitatively characterized, and prescriptions can be made about how the agents should use their computation optimally
System with Self-Interested Agents
Includes computational or human agents Mechanism (e.g., rules of an auction) specifies
legal actions for each agent & how the outcome is determined as a function of the agents’ strategies
Strategy (e.g., bidding strategy), Agent’s mapping from known history to action
Rational self-interested agent chooses its strategy to maximize its own expected utility given the mechanism-- strategic analysis required for robustness -- noncooperative game theory
System with Self-Interested Agents
Computational Complexity
In executing the mechanism In determining the optimal strategyIn executing the optimal strategy
Has significant impact on prescriptions.
Has received little attention in game theory.
Ecommerce Process
1. Interest generation
2. Finding
3. Negotiating
4. Contract execution
5. After sales
MAS in Different EC Stages
1. Interest generation-- Funded adlets that coordinate-- Avatars for choosing which ads to read-- Customer models for choosing who to send ads and how much $ to offer
2. Finding-- Simple current systems: BargainFinder, Jango-- Meta-data, XML-- Standardized feature lists on goods to allow comparison-- How do these get (re)negotiated Different vendors prefer different feature lists Shopper agents need to understand the new lists How do algorithms cope with new features?-- Want to get a bundle: need to find many vendors
MAS in Different EC Stages
3. Negotiating-- Advantages of dynamic pricing: Right things sold to (and bought from) right parties at right time. So, world becomes a better place (social welfare increases)-- Further advantages from discriminatory pricing: Can increase social welfare.-- Fixed-menu take-it-or-leave-it offers -> negotiation Cost of generating & disseminating catalogs? Other customers see the price? Negotiation overhead? Personalized menus (check customer’s web page, links to & from it, what other similar customers did, customer profiles) Generating/printing the menu may be intractable, Negotiation will focus the generation, but vendor may bias prices & offerings based on path-- Preferences over bundles-- Coalition formation
MAS in Different EC Stages
4. Contract execution
-- Digital payment schemes-- Safe exchange
5. After sales
Quantification
Reason:Some concepts, like ‘Good’, ‘Bad’ is hard to comprehend by computer.
Method:Use real numbers (utility) to instead.
Decision Making
S: a set on environment statesD: a set of possible decisionsR: a set of achievable results
Result is influenced by both decision and environment state.
Decision Making
∵Environment state is usually uncertain.
∴For each s∈S there is a probability of occurrence of s.
∴With the mapping M this distribution for each d ∈ D induces a distribution on R.
∴So making the best decision mean choosing the "best" distribution on R among those available.
Decision Making
Example: A Picnic Decision
D={I, O}I: Picnic IndoorO: Picnic Outdoor
S={T, C}T: Thunderstorm Weather Forecast: P(T)=0.3C: Clear P(C)=0.7
R={A, B, G, E}A: AwfulB: BadG: GoodE: Excellent
Decision Making
Example: A Picnic Decision
Definition of M:M(T, O) = AM(T, I) = BM(C, I) = GM(C, O) = E
Decision Making
Example: A Picnic Decision
For Indoor: 30% Bad 70% Good
For Outdoor: 30% Awful 70% Excellent
Decision Making
Utility Function & Expected Utility
Utility Function: U(ri), ri∈Re.g.:U(A)=0, U(B)=2, U(G)=5, U(E)=10
Expected Utility: u(d), d∈Du(d) = ∑ P(ri) * U(ri)
Game Theory
Problem: In a game, players will get different outcomes by using different strategies. What strategies should they choose for improving their outcomes?
Game Theory
Dominant Strategy:
In some games, a player can choose a strategy that "dominates" all other strategies in his strategy set: Regardless of what he expects his opponents to do, this strategy always yields a better payoff than any other of his strategies.
Game Theory
Dominant Strategy Equilibrium:
It is a strategy profile where each agent has picked its dominant strategy.
Game Theory
Nash Equilibrium:
No players can increase their utility by changing their strategies.
3, 3 5, 0
0, 5 1, 1
A B
A
B
Play1
Pla
y2
Play1’s Income Play2’s Income
Nash Equilibrium Point
Game Theory
Criticisms of Nash Equilibrium
-Not unique in all games.
-Does not exist in all games.
-May be hard to compute.