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Automated Negotiation

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Automated Negotiation

Lecture 1: Introduction and Background Knowledge

- 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

- 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 quantitativelycharacterized, and prescriptions can be made about how the agents should use their computation optimally

- 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 analysisrequired for robustness -- noncooperative game theory

- Computational ComplexityIn executing the mechanism In determining the optimal strategyIn executing the optimal strategy
- Has significant impact on prescriptions.Has received little attention in game theory.

- 1. Interest generation
- 2. Finding
- 3. Negotiating
- 4. Contract execution
- 5. After sales

- 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)negotiatedDifferent vendors prefer different feature listsShopper agents need to understand the new listsHow do algorithms cope with new features?-- Want to get a bundle: need to find many vendors

- 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 -> negotiationCost 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

- 4. Contract execution-- Digital payment schemes-- Safe exchange
- 5. After sales

- Utility----Quantification of Decision Result
- Game Theory----Modelling of Decision

- Reason:Some concepts, like ‘Good’, ‘Bad’ is hard to comprehend by computer.
- Method:Use real numbers (utility) to instead.

S: a set on environment states

D: a set of possible decisions

R: a set of achievable results

Result is influenced by both decision and environment state.

- M: S x D ----> R
- R = M (s, d), s∈S, d∈D

- ∵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.

- Example: A Picnic DecisionD={I, O}I: Picnic IndoorO: Picnic Outdoor S={T, C}T: Thunderstorm Weather Forecast: P(T)=0.3C: Clear P(C)=0.7R={A, B, G, E}A: AwfulB: BadG: GoodE: Excellent

- Example: A Picnic DecisionDefinition of M:M(T, O) = AM(T, I) = BM(C, I) = GM(C, O) = E

- Example: A Picnic DecisionFor Indoor: 30% Bad 70% GoodFor Outdoor: 30% Awful 70% Excellent

- Utility Function & Expected UtilityUtility Function: U(ri), ri∈Re.g.:U(A)=0, U(B)=2, U(G)=5, U(E)=10Expected Utility: u(d), d∈Du(d) = ∑ P(ri) * U(ri)

- Example:u(I) = 0.3 * 2 + 0.7 * 5 = 4.1u(O) = 0.7 * 10 = 7

- Problem: In a game, players will get different outcomes by using different strategies. What strategies should they choose for improving their outcomes?

Play1

A B

A

B

Play2

Play1’s Income

Play2’s Income

- Matrix Form:

- Extensive Form:

Play 1

A

B

Play 2

A

B

A

B

3, 3

0, 5

5, 0

1, 1

- 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.

- Dominant Strategy Equilibrium:It is a strategy profile where each agent has picked its dominant strategy.

Play1

A B

A

B

Play2

Play1’s Income

Play2’s Income

Nash Equilibrium Point

- Nash Equilibrium:No players can increase their utility by changing their strategies.

- Criticisms of Nash Equilibrium-Not unique in all games.-Does not exist in all games.-May be hard to compute.

- Existence of Nash EquilibriumAny finite gamewhere each action node is alone in its information set, i.e. at every point in the game, the agent whose turn it is to move knows what moves have been played so far. And the game is dominance solvable by backward induction.