www.monash.edu.au

Monash University

Semester 1, March 2005

Intelligence in Service Oriented Systems

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Learning ObjectivesLearning Objectives

• An understanding of the phases and market structures in electronic environments

• An understanding of the current state-of-the-art in web services

• Ability to reason about the notion of “intelligence” in e-commerce/service environments

• Familiarisation with some AI techniques used to implement “intelligence” in these environments

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Lecture OutlineLecture Outline

• Background - CBB Model, Auction Models• Evolution of the Internet • Web Services

• Intro, Building Blocks, Architecture• Intelligence in e-commerce/services

• Filtering, Recommendation, Selection, Negotiation, Personalisation, Prediction of Service Levels, Semantics, Anything Else ???

• Why Software Agents, When Software Agents

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Consumer Buying Behaviour (CBB) ModelConsumer Buying Behaviour (CBB) Model

• Need Identification• Product Brokering

• what to buy, review of products• Merchant Brokering

• whom to buy from, review of merchants/vendors/WSPS• Negotiation• Purchase and Delivery• Service and Evaluation

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Electronic MarketplacesElectronic Marketplaces

• Electronic Marketplace: Suppliers, Dealers, Buyers and Brokers

• Transaction Phases– Information Phase – buyer collects information from

many prospective suppliers

– Negotiation Phase – buyer and supplier negotiate the conditions of the transaction

– Execution Phase – Actual exchange of goods

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Electronic MarketplaceElectronic Marketplace

• Market Structures• Direct Search Markets

– Buyer directly contacts different suppliers

– Buyer has to perform the entire information phase

– Time consuming and expensive

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Electronic MarketplaceElectronic Marketplace

• Brokered Markets

– Brokers perform the search for a certain fee

• Dealer Markets– Dealers are required to buy products in advance

and offer them at set prices to buyers

– Buyer asks different dealers for prices and immediately buys the product at the cheapest dealer

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Electronic MarketplacesElectronic Marketplaces

• Auction Markets

– Centralise supplies and demands on a single virtual market place

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Auction MechanismsAuction Mechanisms• Valuations – how the value of the item is formed

• Private value auctions• Value depends on user preferences (e.g. buying a

cake)• Common value auctions

• Value depends completely on other users’ values (e.g. buying treasury bonds)

• Correlated value auctions• Value depends on both self and other users (e.g.

buying a Picasso)

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Auction ProtocolsAuction Protocols

• English – first-price open-cry• Each bidder is free to raise his bid• When no bidder is willing to raise, the auction ends with

the highest bidder winning

• Sealed Bid – first-price sealed bid• Each bidder submits one bid without knowing the other

bids• The highest bidder wins and pays the amount of his own

bid

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Auction ProtocolsAuction Protocols

• Dutch – descending• Seller continuously lowers the price until one of

the bidders takes the price at the current price

• Vickery – second-price sealed-bid• Each bidder submits one bid without knowing

the other bids• The highest bidder wins, but pays the price of

the second highest bid

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Evolution of the WWWEvolution of the WWW

• Information Dissemination => Products => Services

• B2C => B2B

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Web Services & Semantic Web ServicesWeb Services & Semantic Web Services

Refer Slides from Tutorial ByJorge Cardoso1, Christoph Bussler2, Amit Sheth1, 4 , Dieter Fensel3 1LSDIS Lab, Computer Science, University of Georgia2Oracle Corporation3 Universität Innsbruck 4 Semagix, Inc

Tutorial at Federated ConferencesOn the Move to Meaningful Internet Computing and Ubiquitous Computer 2002, Irvine CA, October 2002.

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Intelligence in E-Commerce/Web ServicesIntelligence in E-Commerce/Web Services

• Filtering• Recommendation• Selection• Negotiation • Personalisation• Prediction of Service Levels• Semantics• Anything Else ???

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Why and When Software AgentsWhy and When Software Agents

• Why ?• Ease of use, remove tedium, automation• Notion of agency fits well• Emerging technologies facilitate use of agents

• When?• How much time and/or money can be saved if a certain process was

partially automated ?

• Comparing products from different vendors

• How easy is it to express your preferences for the task?

• Shopping for a gift

• What are the risks of an agent making a sub-optimal decision?

• Buying a car, Stock market buying and selling

• What are the consequences of missed opportunities?

• Not being able to effectively monitor new job postings

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When Software AgentsWhen Software Agents

• Rule of Thumb:• the greater the time and money saved thru

automation• the easier it is to express preferences• the lesser the risks of making a sub-optimal

decision• the lesser the loss for missed opportunities• …… the more appropriate it is for using agents

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Agent-Centric CBB StagesAgent-Centric CBB Stages

• Product Brokering

• Merchant Brokering

• Negotiation

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Product BrokeringProduct Brokering

• What to buy • Critical evaluation of the retrieved product information• Agent Systems – Personal Logic, Firefly and Tete-a-Tete• PersonalLogic

• Identify products that best meet consumer needs by guiding them through a large product feature space

• Constraint Satisfaction Problem (CSP)• Filter out unwanted products by allowing specification of constraints

on product features• Remove products that don’t meet hard constraints• Prioritise the rest based on soft constraints

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Product BrokeringProduct Brokering

• FireFly• Word of mouth recommendation system• Automated Collaborative Filtering (ACF)• Compare a shopper’s product ratings with other

shoppers• Identify “nearest shopper neighbours” - users with

similar tastes• Currently used for music and books

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Merchant BrokeringMerchant Brokering

• Compares merchant alternatives• Agent Systems – Andersen Consulting’s BargainFinder, Jango and

MIT Media Lab’s Kasbah• BargainFinder

• First shopping agent for on-line price comparisons• Given a specific product it requests from different merchant web-

sites• Limited proof-of-concept• Interesting findings:

• One-third of on-line CD merchants blocked BargainFinder• AC was receiving inclusion from little-known merchants

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Merchant BrokeringMerchant Brokering

• Jango• Advanced version of BargainFinder• Solved the “blocking issue”

• Issuing product requests from customer’s web-browser rather than a centralised server

• Modus Operandi • Shopper identifies product• Jango simultaneously queries merchant sites (from a

list that is maintained by Excite)• Performs price comparison

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Kasbah – MIT Media LabKasbah – MIT Media Lab

• On-line multi-agent classified ad-system• Modus Operandi

• User who wants to buy or sell creates an agent• Specifies its objectives • Sends it to a centralised agent market-place• Agents proactively seek out potential buyers/sellers

and negotiate with them on behalf of their owners• Agent’s goal – complete an acceptable deal subject to a

set of user specified constraints

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Kasbah – MIT Media LabKasbah – MIT Media Lab

• Buyer/Seller Agent Constraints • Date to buy/sell the item by• Desired Price• Highest/Lowest Acceptable Price• Decay Function –

• allows controlling the negotiation strategy of the agent• Specify how to vary the price as time goes by

• Anxious linear• Cool-headed Quadratic • Frugal Exponential

• Get user approval before finalising deal• Send email notification when agreement is reached

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Kasbah – MIT Media LabKasbah – MIT Media Lab

• KASBAH implementation

• CLOS (Common Lisp Object System)

• Agents support the following methods:

• accept-offer?(agent, from-agent, offer)

• what-is-price?(agent, from-agent)

• what-is-item?(agent, from-agent)

• add-sell-agent

• add-buy-agent

• add-potential-customers(sell-agent, potential-customers)

• add-potential-sellers(sell-agent, potential-sellers)

• Remove operations

• agent-terminated(marketplace, agent)

• deal-made(marketplace, sell-agent, buy-agent, item, price)

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NegotiationNegotiation

• Determination of price and other terms of the transaction• AuctionWeb and OnSale

• Commercial• Sell refurbished second hand goods• Consumers manage their own negotiation strategies

• AuctionBot• General purpose internet auction server• Univ. of Michigan• Modus Operandi

• Create new auctions by choosing• Type of auction• Auction Parameters – clearing times, method for resolving ties in bids, no.

of sellers permitted etc.• Seller sets a base price • Buyers bid according to the protocols of the auction

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NegotiationNegotiation

• Kasbah• Straightforward negotiation• Match buying and selling agents• Only valid negotiation

• Buying agents offer a bid (no time / price restriction)• Selling agents respond with “Yes” or “No”

• Tete-a-Tete (MIT Media Labs)• Negotiation across multiple terms of a transaction

• Warranties, delivery times, service contracts, return policies, loan options, other value added services

• Multi-agent bilateral bargaining (like Kasbah)• Based on product constraints (from the product and merchant brokering

stages)• Multi Attribute Utility Theory (MAUT) • Integrates the product brokering, merchant-brokering and negotiation stages of

the CBB

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Comparison of E-Commerce Agent SystemsComparison of E-Commerce Agent Systems

Personal Logic

Firefly Bargain

Finder

Jango Kasbah AuctionBot

Tete-a-Tete

Need Identification

Product Brokering

X X X X

Merchant Brokering

X X X X

Negotiation X X X

Purchase & Delivery

Product Service & Evaluation

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AI Technologies for Agent Mediated E-CommerceAI Technologies for Agent Mediated E-Commerce

• Recommender Systems• Content-based Filtering• Collaborative Filtering• Constraint-based Filtering

• Negotiation • Constraint Satisfaction Problem • Multi Attribute Utility Theory

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Content-Based FilteringContent-Based Filtering

• Process information from various sources• Extract useful features and elements about its content• Techniques can vary in complexity• Keyword- based Search

• Simple• Matching different combinations of keywords

• Extracting semantic information from documents• Associative networks of keywords in a sentence• Directed graphs of keywords that form sentences

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Content-Based FilteringContent-Based Filtering

Systems: BargainFinder and Jango• Get info. From different web-sites• Adapt their interactions based on different interfaces

• No standard way of presenting information• Wrappers to transform information from a specific web-

site into a common format• BargainFinder – Wrappers hand coded by AC

consultants – Problems ????• Jango – Automatic – Generalised Queries – 50%

success rate

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Collaborative FilteringCollaborative Filtering

• Use ratings and feedback from consumers to filter out irrelevant info.

• No analysis / understanding of the the features or description of the products

• Create a “likeability” index per product• Not a global index• Created for each user dynamically using the profile of

similar users• Which system ?

• Wake up call #1

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Constraint-based FilteringConstraint-based Filtering

• Uses features of items to determine their relevance• Content Vs. Constraint

• Content – accesses data in native format (HTML etc.)• Constraint – requires the problem and solution to be

formulated in terms of variables, domains and constraints

• Any Constraint Satisfaction Problem (CSP) Algorithm can be used

• Which systems ?• Wake up call 2!!!

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Constraint Satisfaction Problem (CSP)Constraint Satisfaction Problem (CSP)

• A BRIEF introduction• Several general purpose and powerful techniques can be used to find

a solution• Finite Domain CSP

• Composed of 3 main parts• A finite set of variables• Each variable is associated with a finite domain• A set of constraints

• Defines relationships among variables • Restricts the values the variables can simultaneously take

• CSP engine assigns a value to each variable while satisfying all the constraints

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Constraint Satisfaction Problem (CSP)Constraint Satisfaction Problem (CSP)

• Define “hard” and “soft” constraints• Examples ?

• Benefit • Can easily explain selection process• Predictable behaviour• Builds trust

• Distributed Constraint Satisfaction Problem (DCSP)• Similar to CSP• Variables and Constraints are distributed over many agents• Agents communicate to jointly solve a problem• Consumers / Sellers can have their respective constraints and

variables

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CSP GraphsCSP Graphs

• A CSP graph is defined as follows: • variables are represented as nodes • constraints between them are represented as

edges• labels of the edges define the constraints • labels of the nodes represents the domain of

the variables. • Allows applying techniques and other features of

graph theory to CSPs.

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Solving CSP’sSolving CSP’s

• Simple Backtracking• Backjumping• Conflict-directed Backjumping• Graph-Based Backjumping• Backmarking• Forward Checking• Backmarking Hybrids• Forward Checking Hybrids• Maintaining Arc Consistency• Dynamic Variable Ordering

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Simple BacktrackingSimple Backtracking

• Instantiate each variable

• for each instantiation

check consistency

• If all checks succeed

Instantiate next variable

• Else another instantiation with the next value is done

• If all possible instantiations for one variable are failed then a backtrack is done to the most recently instantiated variable.

• When the last variable is instantiated with success, then a solution has been found.

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Multi-Attribute Utility TheoryMulti-Attribute Utility Theory

• Decision analysis technique

• CSP’s qualitative, MAUT is a quantitative approach

• Not a protocol for negotiation

• A theory that allows quantitative analysis for negotiation

• Basic Principle:

• Maximise a function based on various criteria

• Usually works by assigning a utility value for each variable under consideration and then computing a utility function from that

• Example: Ask users to grade the importance of product features on a scale of [0,1]

• 1 = required, 0 = no preference

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NegotiationNegotiation

• Agents with different goals interact using some form of “negotiation”

• Negotiation• Process by which a joint decision is reached by 2 or

more agents• Each agent has an individual goal/objective

• Procedure• Communicate their positions (may be conflicting)• Move towards agreement by making concessions or

searching for alternatives

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NegotiationNegotiation

• Principal Features

• Language

• Protocol

• Decision Process

• Designing Systems and Techniques for Negotiation

• Environment Centric

• Focus on the rules of the environment so that agents can interact productively

• Agent Centric

• Focus on the best strategy for the agent to interact

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Properties of an Ideal NegotiationProperties of an Ideal Negotiation

• Efficiency• Agents should not waste resources in coming to an

agreement

• Stability• Agents should not have incentives to deviate from agreed

upon strategies

• Simplicity• Low computational and bandwidth usage for negotiation

• Distribution• No central decision-maker

• Symmetry• No bias against any agent

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EnvironmentsEnvironments

• Task Oriented Domains

• A set of agents with a task to achieve

• All resources needed are available

• Agents can achieve tasks without help or interference from each other

• However, agents may benefit by sharing some tasks

• State Oriented Domains

• Each agent wants to move the “world” to one of its goal states

• Scope for conflict

• Competition over resources

• Goals may not coincide

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EnvironmentsEnvironments

• Worth Oriented Domains• Agents assign a worth (or utility ) to each state• Generalisation of State Oriented Domains• Allows compromising of goals

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Task Oriented ScenarioTask Oriented Scenario

• A set of agents- each with a set of documents it must download from the Internet

• A cost associated with downloading – which agent would like to minimise

• Method of Negotiation?

• Each agent declares documents it wants

• Documents found to be common to 2 or more agents are assigned to one of those agents at random

• Agents pay for the documents they download

• Agents are granted access to any document they download as well as any in their common sets

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Evaluating the ScenarioEvaluating the Scenario

• Simple, Symmetric, Distributed and Efficient (no document is downloaded twice)

• Stability• No constraint for agents to be truthful• But does it pay for agents to lie?• Since there is incentive for agents to diverge from

the optimal strategy – the protocol is stable

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Agent Centric ApproachAgent Centric Approach

• Given an environment, what is the best strategy to follow

• But optimality depends on the negotiation protocol itself• Few general principles

• Example Assumption• Optimal strategy for an agent is “economic

rationality” – • Economic Rationality – maximise its own utility

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Economically Rational NegotiationEconomically Rational Negotiation

• Set of agents is small• Common language• Common problem abstraction• Must reach a common solution

• Unified Negotiation Protocol • Developed by Rosenschien and Zlotkin (1994)• Agents must create a deal which satisfies all their goals• Utility = Amount - Cost• Each agent aims to maximise its own utility• Agents can discuss options (negotiation set)

• All deals have a non-negative utility for every agent• Pareto-optimal

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Casting in Task Oriented TermsCasting in Task Oriented Terms

• Consider a task oriented domain• <T,A,c>• T is a set of tasks, A is a set of agents and c(X) is a function for the

cost of executing the set of tasks X

• Each agent AK has an initial set of assigned tasks TK

• Since each agent can perform its own tasks

• Cost = c(TK )

• Deal

• A redistribution of tasks such that an agent AK now performs tasks dK rather thanTK

• Utility = c(TK ) - c(dK )

• Reduction in costs it experiences from participating in the deal rather than its original assignment

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Evaluating DealsEvaluating Deals

• Obviously, not all distribution of tasks are beneficial to all agents

• A deal d is said to individually rational for an agent AK if

• UK(d ) 0

• So d is beneficial• Pareto-Optimality

• A deal d is pareto-optimal when..

• There is no other deal d1 for which some agent gains and no agent looses

• Negotiation Set is the set of deals that are pareto-optimal and individually rational

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Multi-Attribute NegotiationMulti-Attribute Negotiation

Deals involve several criteria• For each attribute an agent needs a utility function• Plus it must weigh the utilities of all attributes

against each other

• Utility for a Multi-Attribute Object

• For each attribute ai there is a utility function Ui() and a weight wi

• Utility for Negotiation = U(A) = )(aw i

ii

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Multi-Attribute NegotiationMulti-Attribute Negotiation

• Advantages• Global Utility function can use the individual

utilities of the attributes and their weights to allow trade-offs

• Disadvantages• Great theory, hard to practice• Utility and relative weights are highly subjective