Specification of Policies for Web Service Negotiations Steffen Lamparter and Sudhir Agarwal Semantic Web and Policy Workshop Galway, November 7 th University of Karlsruhe (TH)

SWPW – November 7 th, 2005 Outline Motivation Modeling preferences: Utility theory Preferences and Policies – Policy Ontology – Preference Modeling Conclusion Open problems / Outlook

SWPW – November 7 th, 2005 Motivation “I need a service with encryption key ≥ 128 bits, response time < 10s and price < ´5 Euro ” encryption key ≤ 512 bits response time = 5s price = 3 Euro  Web services are highly configurable products  Attribute value pairs are insufficient to describe offers and requests Agent WS Provider I encryption key = 128 bits response time = 3s price = 4 Euro WS Provider II Automatic selection as well as negotiation requires:  Preference information within the valid range  Cardinal preferences to make multi-attributive decisions

SWPW – November 7 th, 2005 Representing Preferences Multi-attribute utility theory – Scoring function maps attribute values to a numerical measure – This measure is comparable and can be aggregated  Classical optimization algorithms can be used  Allows realizing trade-offs (good & expensive vs. bad & cheap) – Allows weighting of attributes – Allows aggregation and weighting of preference functions for one attribute

SWPW – November 7 th, 2005 Policies vs. Utility Functions Policies express preferences! Policies specify the allowed attribute range (e.g. encryption key < 512 bits) Which attribute value is preferred (e.g. 128 bits or 512 bits)? u(x) bits ∞ 128 ≤ encryption key ≤ 512 longer keys are preferred 128 ≤ encryption key ≤ 512 u(x) bits ∞

SWPW – November 7 th, 2005 DOLCE-based Policy Framework DOLCE used as modeling basis – Reuse of modules Description and Situation, Ontology of Plans, Ontology of Information Objects Privacy Policy WS Provider store Private data Storage Duration {1,2,…,14} {7}WS Invocation

SWPW – November 7 th, 2005 Modeling Utility Information  Adding primitives for utility modeling  degree yl pv

SWPW – November 7 th, 2005 Modeling Utility Information  represents the points (x,y) that form the utility function Change Policy Value to a subclass of   restricted to piecewise linear functions Satisfiability defines the degree a Situation Value satisfies the Policy Value YL contains an instance for each line in the function  u(x)

SWPW – November 7 th, 2005 Policy Evaluation Aggregation functions such as SUM, MIN, MAX, etc. are required  Ontology formalism ALC(  ) [Baader,Sattler 03] Deriving utility for a concrete Situation Value P = (satisfies ± degree,  yl ±  u(x) bits ∞ satisfies degree  yl

SWPW – November 7 th, 2005 Policy Evaluation Calculation of the overall utility according to 1. Weighted degree of satisfaction (wds) is calculated by P * (wds ± degree, satisfies ± degree, i j )  True iff wds ± degree = (satisfies ± degree) * weight holds 2. wds of attributes are aggregated to the overall utility P = (degree,  a j ± wds ± degree) GoodService v Service u 9 >(0.7,degree)

SWPW – November 7 th, 2005 Conclusion Bringing together two powerful paradigms: Policy-based computing and utility theory  Enables automated selection of services and negotiation of service parameters Preference information is modeled using DL  Facilitates interoperability in open and heterogeneous environments  Reuse of existing DL-reasoners  Preference information can be used within the reasoning process

SWPW – November 7 th, 2005 Open Problems / Outlook Checking for satisfiability and subsumption in ALC(  ) may lead to undecidability [Baader,Sattler 03] Specifying policies gets even harder… – Approximate preferences from existing policies [Lamparter et. al. 05] – There are 30 years of work in the field of decision analysis and preference elicitation [Keeney, Raiffa 76]  Support policy specification by reusing of existing preference elicitation techniques

SWPW – November 7 th, 2005 References [Baader, Sattler 03] Franz Baader, Ulrike Sattler: Description logics with aggregates and concrete domains. Information Systems 28(8): (2003) [Keeney, Raiffa 76] Keeney, R.L. & Raiffa, H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. J. Wiley, New York, 1976 [Lamparter et. al. 05] Lamparter, S., Eberhart, A., Oberle, D.: Approximating service utility from policies and value function patterns. In: 6th IEEE Int. Workshop on Policies for Distributed Systems and Networks, IEEE Computer Society (2005)

SWPW – November 7 th, 2005 Thank you!