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# Multi-Attribute Preference Logic - PowerPoint PPT Presentation

Multi-Attribute Preference Logic. Koen V. Hindriks, Wietske Visser, Catholijn M. Jonker. Overview. Motivating Context Informal Example Objectives Intuitions Multi-Attribute Preference Logic. Making the machine a negotiation partner. Pocket Negotiator project

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### Multi-Attribute Preference Logic

Koen V. Hindriks, Wietske Visser, Catholijn M. Jonker

• Motivating Context

• Informal Example

• Objectives

• Intuitions

• Multi-Attribute Preference Logic

Making the machinea negotiation partner

• Pocket Negotiator project

• Synergy between man and machine

• Provides support for negotiating

• Various Components:

• Negotiating strategy

• Preference elicitation

• Preference representation

• Emotions in negotiation

• We are interested in:

Qualitative Preference Representation and Reasoning

GENIUS

Environment

http://mmi.tudelft.nl/negotiation

GENIUS = Java-Based Negotiation Environment

http://www.anac2011.com

Deadline for agent submissions January 31st, 2011

Suppose we want to buy a house.

Also suppose we’re considering three houses (objects):

• house1, house2 and house3.

The properties that are important (in the order presented):

• we can afford the house (affordable),

• it is close to our work (closeToWork), and

• it is large (large).

Now, suppose we have an assignment of houses to properties.

We’d like to figure out which house is most preferred.

• Provide a logical framework for reasoning with multi-attribute preferences.

• Capture and formalize different preference ordering strategies

• Built on and extend existing work on preference logics

Observation:

… and note that preferences for these objects are commonly derived from ranked sets of properties (attributes) associated with these objects.

• It seems clear that we would prefer house1 over the other two

• But what about the relative preference of house2 and house3?

consider the most important properties in order

house1 > house2 > house3

Other orderings: best-out, discrimin, …

• As usual, properties can be represented by sets of worlds

• We also represent objects as (sets of) world(s)

• Key to a logic of multi-attribute preferences is the representation of property rankings

• A logic for qualitative multi-attribute preferences

• Facilitates reasoning with property rankings and to formalize associated strategies for deriving object preferences from such rankings

• Propositional, modal logic

• Special propositions i that can be used to refer to sets of worlds (=objects; compare with hybrid logic)

• Operator represents that φ is true in all worlds preferred over the current one

• Operator represents that φ is true in all words not equally preferred to the current one

• Operator represents the usual universal operator in ML

• operators extend preference logic of Girard (2008)

• Read pref(i,j) as object i is preferred over j

• Definitions for τ, κ, # orderings

• Introduced modal logic for expressing ordering strategies for multi-attribute preferences

• Showed that other preference formalisms (ranked knowledge bases) can be translated into multi-attribute preference logic

• Future work:

• Partial orderings

• Currently logic supports binary preferences

• Axiomatization