Shirin Sohrabi

Representing and reasoning about preferences in requirements engineering

The priorities that stakeholders associate with requirements may vary from stakeholder to stakeholder and from one situation to the next. Differing priorities, in turn, imply different design decisions for the system to be. While elicitation of requirement priorities is a well-studied activity, modeling and reasoning with prioritization has not enjoyed equal attention. In this paper, we address this problem by extending a state-of-the-art goal modeling notation to support the representation of preference (“nice-to-have”) requirements. In our extension, preference goals are distinguished from mandatory ones. Then, quantitative prioritizations of the former are constructed and used as criteria for evaluating alternative ways to achieve the latter. To generate solutions, an existing preference-based planner is utilized to efficiently search for alternatives that best satisfy a given set of mandatory and preferred requirements. With such a planning tool, analysts can acquire a better understanding of the impact of high-level stakeholder preferences on low-level design decisions.

Web service composition via generic procedures and customizing user preferences

We claim that user preferences are a key component of Web service composition – a component that has largely been ignored. In this paper we propose a means of specifying and intergrating user preferences into Web service composition. To this end, we propose a means of performing automated Web service composition by exploiting generic procedures together with rich qualitative user preferences. We exploit the agent programming language Golog to represent our generic procedures and a first-order preference language to represent rich qualitative temporal user preferences. From these we generate Web service compositions that realize the generic procedure, satisfying the user’s hard constraints and optimizing for the user’s preferences. We prove our approach sound and optimal. Our system, GologPref, is implemented and interacting with services on the Web. The language and techniques proposed in this paper can be integrated into a variety of approaches to Web or Grid service composition.

Integrating Preferences into Goal Models for Requirements Engineering

Requirements can differ in their importance. As such the priorities that stakeholders associate with requirements may vary from stakeholder to stakeholder and from one situation to the next. Differing priorities, in turn, imply different design decisions for the end system. While elicitation of requirements priorities is a well studied activity, though, the modeling and reasoning side of prioritization has not enjoyed equal attention. In this paper, we address this by extending a traditional goal modeling notation to support the representation of optional and preference requirements. In our extension, optional goals are distinguished from mandatory ones. Then, quantitative prioritizations of the former are constructed and used as criteria for evaluating alternative ways to achieve the latter. A state-of-the-art preference-based planner is utilized to efficiently search for alternatives that best satisfy the given preferences. This way, analysts can acquire a better understanding of the impact of high-level stakeholder preferences to low-level design decisions.

Web Service Composition via the Customization of Golog Programs with User Preferences

We claim that user preferences are a key component of effective Web service composition, and one that has largely been ignored. In this paper we propose a means of specifying and intergrating user preferences into Web service composition. To this end, we propose a means of performing automated Web service composition by exploiting a flexible template of the composition in the form of a generic procedure. This template is augmented by a rich specification of user preferences that guide the instantiation of the template. We exploit the agent programming language Golog to represent our templates as Golog generic procedures and we exploit a first-order preference language to represent rich qualitative temporally-extended user preferences. From these we generate Web service compositions that realize a given generic procedure, satisfying the users hard constraints and optimizing for the users preferences. We prove our approach is sound and optimal. Our system, GologPref, is implemented and interacting with services on the Web. The language and techniques proposed in this paper can be integrated into a variety of approaches to Web or Grid service composition.

Optimizing Web Service Composition While Enforcing Regulations

To direct automated Web service composition, it is compelling to provide a template, workflow or scaffolding that dictates the ways in which services can be composed. In this paper we present an approach to Web service composition that builds on work using AI planning, and more specifically Hierarchical Task Networks (HTNs), for Web service composition. A significant advantage of our approach is that it provides much of the how-to knowledge of a choreography while enabling customization and optimization of integrated Web service selection and composition based upon the needs of the specific problem, the preferences of the customer, and the available services. Many customers must also be concerned with enforcement of regulations, perhaps in the form of corporate policies and/or government regulations. Regulations are traditionally enforced at design time by verifying that a workflow or composition adheres to regulations. Our approach supports customization, optimization and regulation enforcement all at composition construction time. To maximize efficiency, we have developed novel search heuristics together with a branch and bound search algorithm that enable the generation of high quality compositions with the performance of state-of-the-art planning systems.

Preference-based web service composition: a middle ground between execution and search

Much of the research on automated Web Service Composition (WSC) relates it to an AI planning task, where the composition is primarily done offline prior to execution. Recent research on WSC has argued convincingly for the importance of optimizing quality of service, trust, and user preferences. While some of this optimization can be done offline, many interesting and useful optimizations are data-dependent, and must be done following execution of at least some information-gathering services. In this paper, we examine this class of WSC problems, attempting to balance the trade-off between offline composition and online information gathering with a view to producing high-quality compositions efficiently and without excessive data gathering. Our investigation is performed in the context of the semantic web employing an existing preference-based Hierarchical Task Network WSC system. Our experiments illustrate the potential improvement in both the quality and speed of composition generation afforded by our approach.

Publishing bibliographic data on the Semantic Web using BibBase

We present BibBase, a system for publishing and managing bibliographic data available in BiBTeX files. BibBase uses a powerful yet light-weight approach to transform BiBTeX files into rich Linked Data as well as custom HTML code and RSS feed that can readily be integrated within a users website while the data can instantly be queried online on the systems SPARQL endpoint. In this paper, we present an overview of several features of our system. We outline several challenges involved in on-the-fly transformation of highly heterogeneous BiBTeX files into high-quality Linked Data, and present our solution to these challenges.

Customizing the composition of actions, programs, and web ervices with user preferences

Web service composition (WSC) - loosely, the composition of web-accessible software systems - requires a computer program to automatically select, integrate, and invoke multiple web services in order to achieve a user-defined objective. It is an example of the more general task of composing business processes or component-based software. Our doctoral research endeavours to make fundamental contributions to the knowledge representation and reasoning principles underlying the task of WSC, with a particular focus on the customization of compositions with respect to individual preferences. The setting for our work is the semantic web, where the properties and functioning of services and data are described in a computer-interpretable form. In this setting we conceive of WSC as an Artificial Intelligence planning task. This enables us to bring to bear many of the theoretical and computational advances in reasoning about action and planning to the task of WSC. However, WSC goes far beyond the reaches of classical planning, presenting a number of interesting challenges that are relevant not only to WSC but to a large body of problems related to the composition of actions, programs, business processes, and services. In what follows we identify a set of challenges facing our doctoral research and report on our progress to date in addressing these challenges.

IBM Scenario Planning Advisor: Plan Recognition as AI Planning in Practice

We present the IBM Research Scenario Planning Advisor (SPA), a decision support system that allows users to generate diverse alternate scenarios of the future and enhance their ability to imagine the different possible outcomes, including unlikely but potentially impactful futures. The system includes tooling for experts to intuitively encode their domain knowledge, and uses AI Planning to reason about this knowledge and the current state of the world, including news and social media, when generating scenarios.

An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem

Multi-Agent Plan Recognition (MAPR) is the problem of inferring the goals and plans of multiple agents given a set of observations. While previous MAPR approaches have largely focused on recognizing team structures and behaviors, given perfect and complete observations, in this paper, we address potentially unreliable observations and temporal actions. We propose a multi-step compilation technique that enables the use of AI planning for the computation of the probability distributions of plans and goals, given observations. We present results of an experimental evaluation on a novel set of benchmarks, using several temporal and diverse planners.