1 / 21

Preference Elicitation: An Overview

Preference Elicitation: An Overview. Ronen Brafman Computer Science Department Ben-Gurion University, ISRAEL. Intelligence. Readiness. Objective conditions. Political situation. Strategic and tactical intentions and preferences. Budget limitations. Beliefs. Experience.

anneliese
Download Presentation

Preference Elicitation: An Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Preference Elicitation: An Overview Ronen Brafman Computer Science Department Ben-Gurion University, ISRAEL

  2. Intelligence Readiness Objective conditions Political situation Strategic and tactical intentions and preferences Budget limitations Beliefs Experience Outcome evaluation for alternative decisions Campaign-level goals Monitor and filter information Adapt presentation Beliefs and preference refinement through incremental questioning Better decision Proposals of preferable courses of actions

  3. Preference Elicitation: Statement of Problem To make good decisions on behalf of a user we must understand and represent his/her objectives Preference elicitation problem: obtaining a good and useful description of the agent’s objectives Preference elicitation is a well known bottle-neck for decision support and automation systems

  4. The Task • Use: • Natural statements users make • Answers to questions users find intuitive • To generate • good (informative, faithful) and • useful (convenient to reason with) • representation of the user’s preference for the task at hand

  5. CLASSICAL APPROACHES

  6. Utility Functions • Real valued function on the space of possible outcomes • U(o) > U(o’)  o is a better outcome than o’ • Much more – allows evaluating actions • Classical means of expressing preferences • Rich, quantitative representation • Difficult to elicit from users • Over-kill when task essentially deterministic • E.g., search, filtering, display, content

  7. Multi-Attribute Utility Theory - I • Describe a utility function for each aspect of an outcome, independently • e.g., utility of different arrival times, utility of safety of each means of transportation, etc. • Compare outcomes by comparing utilities component-wise • Use dominance relations (Pareto optimality) • Other techniques for qualitative/semi-quantitatve comparisons

  8. Multi-Attribute Utility Theory - II • Additive representation: where a goes over all attributes • Estimation task is much simpler now • Reason: very strong independence assumption – attributes are mutually independent • Classical sources: • Decisions with Multiple Objectives, Keeney & Raiffa (1976) • Foundations of Measurement (Vol. 1), Krantz, Luce, Suppers, & Tversky (1971). • Utility Theory for Decision Making, Fishburn (1969)

  9. Current and New Techniques

  10. Capitalizing on Structure • Bacchus & Grove `96: Generalized additive independence (GAI) Here z goes over sets of (not necessarily disjoint) attributes. Some interesting connection with probabilistic independence, graphical models • Shoham `97: A Bayesian network like notion of utilities. Based on a new concept of utility “factors”. Allows one to benefit from all advantages of BN technology Problematic issue – counterpart of causality for utilities (mental causality?), thus in practice, it is not clear that users can actually describe this. • La Mura & Shoham `01: Expected utility networks Based on multiplicative notion of conditional independence. Interesting, but still has to be studied from the preference elicitation point of view. • Boutilier, Bacchus, and Brafman `01: UCP-nets Special case of GAI. Interesting and useful properties (quantitative CP-nets)

  11. Qualitative Approaches • People are more comfortable making qualitative preference statements (Doyle’s presentation): • “I like this more than that” • “Safety is more important than performance” • Qualitative information can be used to rank options/outcomes • Can be used as a starting point for obtaining a quantitative description (i.e., utilities) • Sufficient for many tasks (e.g., filtering, monitoring, and displaying information)

  12. Conditional Logic of Preference Identifying and exploiting interrelation (and mutual irrelevance) between various parameters of the problem. • Doyle and Wellman (1991):Logic of relative desire • Reason about statements of the form “a is preferred to b” • Adopts ceteris paribus (“all else being equal”) semantics. • Extends the “logic of preference” of von Wright (mainly propositional) • Boutilier (1994) – The Logic CO • Reason about statements of the form “If p then it is better to have q” • Semantics: Any p-world with q is better than any p-world without q, unless over-ruled by a more specific statement. • Reasoning based on propositional non-monotonic logic (various options can be used, no clear “winner”…).

  13. Capitalizing on Structure: CP-nets [Boutilier, Brafman, Hoos, Poole] An intuitive, qualitative, graphical model of preferences, that captures statements of (conditional) preferential independence (Try to get Bayes-nets like benefits for preference reasoning) • Core part of the model is a directed graph: • Each node represents a domain variable. • The immediate parents Parents(X) of a variable Xin the network are those variables that affect user’s preference over the values of X. • Parents(exterior-color) = { vehicle-category } • TCP-nets [Brafman & Domshlak]: a more expressive variant • Associated optimization algorithms

  14. Pants Jacket Shirt

  15. worst best

  16. Day of the flight Departure Time Airline Stop-overs Class

  17. Other Techniqes - I • Minmax Regret (Boutilier et. al.) • Get partial information about utility function • Structure, Bounds, etc. • Find decision minimizing “regret” w.r.t. all utility functions consistent with information • Incremental Elicitation • Try to solve with partial information • Identify specific information required and ask • Examples: • Combinatorial auctions [Sandholm et. al.] • Product search [Blythe et. al., Brafman & Domshlak]

  18. Other Techniques - II • Mixed Qualitative and Quantitative • Get qualitative information • Interpret this as constraints on utility function • Work with some consistent utility function • Can be used in conjunction with incremental elicitation (refining the utility function estimate) • Examples: [Doyle et al.; Brafman & Domshlak] • Preference elicitation as a decision/learning problem (e.g., a POMDP [Chajewska & Koller; Boutilier] ) • Using “problematic” preference information [Domshlak & Brafman, Brafman & Dimopoulos]

  19. Recent Applications • Content display and adaptation [Domshlak et. al., Brafman & Friedman] • Flight selection [Blythe; Brafman & Domshlak, Boutilier et. al.] • Autonomic Computing (resource allocation) [Boutilier et. al.] • Combinatorial Auctions [Sandholm et. al.]

  20. Future • A number of promising directions • Need more multi-disciplinary interaction • Preference workshop AAAI’02 • Upcoming special issue of Comp. Intell. • A Dagstuhl workshop in June 2004 with representatives from Database, AI, Economics, and Philosophy communities • More cooperation, applications, needed

  21. Improvements • Examples, less formulas • Don’t mention less important directions – just the idea • Slide on approaches – very short • Slides on select interesting approaches with examples

More Related