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WELCOME Graduate Student Symposium The 25th Canadian Conference on Artificial Intelligence AI 2012 Welcome to the Graduate Student Symposium (GSS) at the 25th Canadian Conference on Artificial Intelligence (AI-­‐2012)! It gives the organizing committees of GSS-­‐2012 and AI-­‐2012 great pleasure to bring together graduate students, senior researchers, and faculty members from Canadian universities. Every year (since 2009), the GSS is held as a one-­‐day event in conjunction with the AI conference to help graduate students receive support from senior researches in their field. The main objective of the symposium is to allow graduate students to disseminate their new ideas, get constructive feedback from a panel of senior researchers, and eventually produce high quality research outcomes. In keeping with the tradition of inspiring students from success stories of role models and successful researchers both in academia and industry, we are delighted to have Dr. Sheila McIlraith (University of Toronto) as the invited keynote speaker; Dr. Guy Lapalme (Université de Montreal), Dr. Stan Matwin (University of Ottawa), Dr. Marina Sokolova (University of Ottawa), Dr. Eric Charton (Wikimeta), Dr. Mouhammad Barouni-­‐Ebrahimi (IBM Canada), and Dr. Narjès Boufaden (Keatext) as symposium mentors and career panellists. Our thanks to the GSS participants, mentors and career panellists for their contributions to the symposium—We hope you find this a rewarding and stimulating experience. Wishing you a pleasant stay in Toronto! May 27, 2012 York University, Toronto, Ontario Leila Kosseim, AI Co-­‐Chair Department of Computer Science & Software Engineering Concordia University, Montreal [email protected] Diana Inkpen, AI Co-­‐chair School of Electrical Engineering & Computer Science University of Ottawa, Ottawa [email protected] Ebrahim Bagheri, GSS Co-­‐Chair School of Computing & Information Systems Athabasca University, Athabasca [email protected] Jocelyne Faddoul, GSS Co-­‐Chair Faculty of Computer Science Dalhousie University, Halifax [email protected] PARTICIPANTS Graduate Students Abderahman Rashwan Aparna Lalingkar Christian Muise Farid Seifi Houssem Eddine Dridi Jamel Eddine Jridi Khalid Mansour Martin Scaiano Mostafa Karamibekr Shifta Ansari Concordia University International Institute of Information Technology, Bangalore, India University of Toronto University of Ottawa Université de Montréal Université de Montréal Swinburne University of Technology, Australia University of Ottawa University of New Brunswick University of Western Ontario Mentors and Career Panelists Diana Inkpen University of Ottawa Eric Charton Wikimeta, CRIM Guy Lapalme Université de Montréal Leila Kosseim Concordia University Marina Sokolova University of Ottawa Mouhammad Barouni-­‐Ebrahimi IBM Canada Narjès Boufaden KeaText Stan Matwin University of Ottawa AGENDA 8:30 -­‐ 8:55 Registration 8:55 -­‐ 9:00 Opening Remarks 09:00 -­‐ 12:20 Session I 09:00 -­‐ 09:20 Martin Scaiano Populating a Knowledge base from a dictionary Lead Discussant: Marina Sokolova Support Discussant: Guy Lapalme Note Taker: Abderahman Rashwan 9:20 -­‐ 9:40 Khalid Mansour Managing Concurrent Negotiation in Multi-­‐agent Systems Lead Discussant: Marina Sokolova Support Discussant: Diana Inkpen Note Taker: Mostafa Karami 9:40 -­‐ 10:00 Abderahman Rashwan Semantic Analysis of Functional and Non-­‐Functional Requirements in Software Requirements Specifications Lead Discussant: Guy Lapalme Support Discussant: Marina Sokolova Note Taker: Farid Seifi 10:00 -­‐ 10:20 Aparna Lalingkar Ontology Based Intelligent Tutoring for Mathematical Problem Solving Lead Discussant: Diana Inkpen Support Discussant: Stan Matwin Note Taker: Shifta Ansari 10:20 10:50 Coffee Break / Poster Session Mostafa Karami (Mentor: Stan Matwin) Shifta Ansari (Mentor: Diana Inkpen) Farid Seifi (Mentor: Guy Lapalme) Houssem Eddine Dridi (Mentor: Marina Sokolova) 10:50 12:20 Rounds of Elevator Talks 1.Abderahman Rashwan 2.Aparna Lalingkar 3.Christian Muise 4. Farid Seifi 5. Jamel Eddine Jridi 6. Houssem Eddine Dridi 7. Khalid Mansour 8. Martin Scaiano 9. Mostafa Karami 10. Shifta Ansari 2 12:20 -­‐ 13:00 KEYNOTE SPEAKER Lunch / Poster Session Mostafa Karami (Mentor: Guy Lapalme) Shifta Ansari (Mentor: Marina Sokolova) Farid Seifi (Mentor: Diana Inkpen) Houssem Eddine Dridi (Mentor: Stan Matwin) From Services to the Cloud and into Space: Modeling and Customizing Component-­‐Based Systems Sheila McIlraith 13:00 -­‐ 14:10 Invited Talk Sheila McIlraith 14:10 -­‐ 14:40 14:40 -­‐ 15:00 MSc Award Winner Talk 15:00 -­‐ 16:00 Group mingles & Poster Session Group 1: Guy Lapalme, Mostafa Karami, Martin Scaiano, Farid Seifi Group 2: Stan Matwin, Shifta Ansari, Houssem Eddine Dridi Group 3: Marina Sokolova, Aparna Lalingkar, Christian Muise Group 4: Diana Inkpen, Jamel Eddine Jridi, Khalid Mansour, Christian Muise Session II Jamel Eddine Jridi An Ontological Approach to Document Engineering Lead Discussant: Diana Inkpen Support Discussant: Stan Matwin Note Taker: Christian Muise Christian Muise Generating and Executing Plans Lead Discussant: Stan Matwin Support Discussant: Guy Lapalme Note Taker: Houssem Eddine Dridi What do phone apps, business processes, and space vehicles have in common? Seemingly a lot more than one might think at first blush. All are instances of component-­‐based systems – software and/or electro-­‐mechanical systems that are comprised of interoperating component parts. For the last 10 years, much of my research has focused on problems broadly related to the modeling of automated interoperation of component-­‐based systems. One example of such systems is the family of autonomous systems that NASA has been tasked with developing – electromechanical systems that are engineered as interoperating hardware and software components. Another example of such systems is the myriad of programs and devices that need to interoperate automatically to realize the vision of the Semantic Web Services, the so-­‐called Web of Things, and more recently by Grid and Cloud computing. This broad array of seemingly disparate component-­‐based systems share a number of core technical challenges whose resolution has the potential for broad and significant impact. A particular challenge we have addressed is the automated composition of such component devices, programs and information sources in order to achieve some objective. In today’s talk, I’ll focus on some of the work that my students and I have done over the years on modeling and producing personalized compositions of web services, and the broad applicability of the modeling and automated reasoning principles underlying this work. Brief Bio 16:50 -­‐ 17:50 Career Panel Chair: Marina Sokolova Abstract Coffee Break / Poster Session Eric Charton Leila Kosseim Mohammad Barouni Narjès Boufaden Stan Matwin 17:50 -­‐ 18:00 Closing Remarks 3 Sheila McIlraith is an Associate Professor in the Department of Computer Science, University of Toronto. Prior to joining U of T, Prof. McIlraith spent six years as a Research Scientist at Stanford University, and one year at Xerox PARC. McIlraith's research is in the area of knowledge representation and reasoning. She has 10 years of industrial R&D experience developing artificial intelligence applications. McIlraith is the author of over 70 scholarly publications. She is an associate editor of the journal Artificial Intelligence, co-­‐chair of the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR2012), and past program co-­‐chair of the International Semantic Web Conference (ISWC2004). In 2011 McIlraith was appointed a fellow of the Association for the Advancement of Artificial Intelligence (AAAI). McIlraith's early work on Semantic Web Services has had notable impact. In 2011 she and her co-­‐authors were honoured with the SWSA 10-­‐
year Award, recognizing the highest impact paper from the International Semantic Web Conference, 10 years prior. Her research has also made practical contributions to the development of next-­‐generation NASA space systems and to emerging Web standards. 4 ABSTRACTS Populating a Knowledge base from a dictionary Martin Scaiano Research applying logic and reasoning to natural language processing (NLP) requires large costly hand built knowledge resources. Time, effort, and the cost of producing these resources usually leads to limited coverage. There is a need to extract knowledge from textual knowledge resources, such as dictionaries, encyclopedias, and textbooks and then convert it into a format usable by reasoning NLP systems. My goal is to begin this task with extracting knowledge from dictionaries and demonstrating its utility in NLP tasks. Managing Concurrent Negotiation in Multi-­‐agent Systems Khalid Mansour The one-­‐to-­‐many agent system is a typical multi-­‐agent system that involves interaction between agents through negotiation. The one-­‐to-­‐many negotiation form is a complicated problem especially when the negotiation is about distinct negotiation objects characterized by multiple negotiation issues. The complexity of the problem comes from the existence of many variables in the negotiation process. For example, the number of agents, the number of objects and the number of negotiation issues contribute to the problem complexity. Few existing works address some aspects of the coordination problem in the one-­‐to-­‐many negotiation form. However, most works address simple negotiation scenarios such as negotiation over a single object characterized by a single issue or multiple issues. The aim of this research is to investigate possible coordination problems in the one-­‐
to-­‐many negotiation form and propose effective and robust solutions for a number of negotiation scenarios. We test our solutions by evaluating some negotiation objective criteria such as utility gain, agreement rate etc. Semantic Analysis of Functional and Non-­‐Functional Requirements in Software Requirements Specifications Abderahman Rashwan Software Requirements Specifications (SRS) documents are important artifacts in the software industry. A SRS contains all the requirements specifications for a software system, either as functional requirements (FR) or non-­‐functional requirements (NFR). FRs are the features of the system, whereas NFRs define the quality attributes of the system. NFRs impact the system as a whole and interact both with each other and with the functional requirements. SRS documents are typically written in informal natural language, which impedes their automated analysis. My goal is to support software engineers with semantic analysis methods that can automatically extract and analyze requirements written in natural language 5 texts, in order to (i) make requirements documents machine-­‐processable by transforming them into an ontological representation; (ii) apply quality assurance (QA) methods on the extracted requirements, in order to detect defects, like ambiguities or omissions; and (iii) attempt to build traceability links between NFRs and the FRs impacted by them, in order to aid effort estimation models. Ontology Based Intelligent Tutoring for Mathematical Problem Solving Aparna Lalingkar Studies conducted in Indian metros such as Delhi, Mumbai, Kolkata, Chennai and Bangalore on students’ mathematics learning show that students from top schools perform below the international average. For this paper, mathematical problem refers to a problem that is solved by using mathematical models, formulas, mathematical logic, and rules. In the historical review of problem solving, there is a dichotomy between the terms problem solving and doing exercise. The term problem solving refers to the use of various heuristic strategies, pattern searching, and control functions for selecting the appropriate strategy, whereas doing exercises refers to the use of known procedures and methods. Classroom teaching is more focused on teaching students to do exercises. Despite more than seven decades of work in teaching problem solving, the classroom teaching of solving mathematical problems at the school level has remained a great challenge. The focus of this study is on the teaching of mathematical word problems, and so literature on other kinds of mathematical problem solving has not been considered. An Ontological Approach to Document Engineering Jamel Eddine Jridi The electronic exchange of documents is the mechanism of communication between remote applications. This mechanism describes the exchange and the information shared in a business process. Its major problem is the semantics in documents, information finding and interpreting their relations. XML structures exchanged documents, but without dealing with the semantics of data. Our goal is to propose a model or ontology that considers the semantics, and to extract new information from this data by means of several types of reasoning. The reasoning is done through inference engines, like Pellet and Racer, that check the consistency of our ontology by verifying the non-­‐existence of conflicts in concept definitions. Also, we propose a query support to extract knowledge that help partners make a decision. In this paper we present ideas and research directions that we want to explore in order to develop new approaches and methods for document engineering based on the semantic web. 6 Generating and Executing Plans Christian Muise POSTERS Our work addresses the problem of generalizing a plan and representing it for efficient execution. A key area of automated planning is the study of how to generate a plan for an agent to execute. The plan itself may take on many forms: a sequence of actions, a partial ordering over a set of actions, or a procedure-­‐like description of what the agent should do. Once a plan is found, the question remains as to how the agent should execute the plan. For simple forms of representation (e.g., a sequence of actions), the answer to this question is straightforward. However, when the plan representation is more expressive (e.g., a GOLOG program), or the agent is acting in an uncertain world, execution can be considerably more challenging. We focus on the problem of how to generalize various plan representations into a form that an agent can use for efficient and robust online execution. Shifta Ansari Understanding Tables in Biomedical Journal Articles Farid Seifi Improving Attribute Clustering in the Absence of Labeled Data, An Iterative Semi-­‐supervised Attribute Clustering Approach Houssem Eddine Dridi An Approach for Analyzing Textual Data in Microblogs Moustafa Karamibekr A Sentiment Analysis Framework for Social Issues 7 8