Fourteenth Portuguese Conference on Artificial Intelligence

Important Dates

  • Deadline for track proposals: November 15, 2008
  • Notification of track acceptance: January 10, 2009
  • Extended deadline for paper submission: April 29, 2009
  • Notification of paper acceptance: June 15, 2009
  • Deadline for final versions: July 15, 2009
  • Conference dates: October 12-15, 2009





September 16, 2009, Provisional version of Detailed Schedule has been published.

September 15, 2009, General Schedule has been published.

September 8, 2009, list of accepted papers has been announced.

June 19, 2009, registration is open.

June 15, 2009, paper decision was sent.

April 9, 2009, paper submission deadline was extended.

February 10, 2009, announcement of accepted tracks.

February 10, 2009, call for papers was added.

October 7, 2008 call for thematic tracks announcement.

October 6, 2008 site announcement.

Invited speakers

Hod Lipson

The Robotic Scientist: Mining experimental data for dynamical invariants, from cognitive robotics to computational biology

ABSTRACT: For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. By seeking dynamical invariants, we go from finding just predictive models to finding deeper conservation laws. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems. Application to modeling physical and biological systems will be shown.

BIO: Hod Lipson is an Associate Professor of Mechanical & Aerospace Engineering and Computing & Information Science at Cornell University in Ithaca, NY. He directs the Computational Synthesis group, which focuses on novel ways for automatic design, fabrication and adaptation of virtual and physical machines. He has led work in areas such as evolutionary robotics, multi-material functional rapid prototyping, machine self-replication and programmable self-assembly. Lipson received his Ph.D. from the Technion - Israel Institute of Technology in 1998, and continued to a postdoc at Brandeis University and MIT. His research focuses primarily on biologically-inspired approaches, as they bring new ideas to engineering and new engineering insights into biology. For more information visit

Marie-Francine Moens

More than Just Words: Discovering the Semantics of Text with a Minimum of Supervision

ABSTRACT: Humans discover many different aspects of meaning in sources such as text, speech, audio and visual data. We retrieve certain information from them, which is used in our daily actions and decisions. Machines can assist in these tasks when they mine, summarize or aggregate content from the digital sources. We can train pattern recognizers to discover the semantics of the sources, but a major bottleneck is the lack of sufficient annotated examples, as a manual labeling is often prohibitively expensive. A multitude of semantic classes can be recognized from the surface features and the variation of surface features that express a similar meaning is usually high, making the task not easier.

In this talk we explore the possibilities of reducing training examples when recognizing semantics in text. We discuss approaches of unsupervised expansion of the training set, multiple instance learning, active learning and unsupervised language modeling, and study their impact on semantic classification of text. We illustrate the approaches by our own research on relationship detection, semantic role labeling, opinion extraction and argumentation mining. We demonstrate that the large amounts of data that can be cheaply collected (e.g., on the World Wide Web) assist in recognizing the targeted information. Linguistic and cognitive theories can guide us in the selection of appropriate features and seed training examples.

BIO: Marie-Francine Moens is associate professor at the Department of Computer Science of the Katholieke Universiteit Leuven, Belgium. She holds a Ph.D. degree in Computer Science (1999) from this university. She currently leads a research team of 1 postdoctoral fellow and 9 doctoral students, and is currently coordinator of or partner in 7 European or international research projects in the fields of information retrieval and text mining. Her main interests are in the domain of automated content retrieval from texts with a strong emphasis on probabilistic content models obtained through machine learning techniques. Since 2001 she teaches the course Text Based Information Retrieval and since 2009 she partly teaches the courses Natural Language Processing and Current Trends in Databases at K.U.Leuven. She has (co-)authored more than 150 research papers in the field of information retrieval and text analysis, is author of two monographs published in the Springer International Series on Information Retrieval, and is (co-)editor of several books. She is the (co-)organizer of 3 editions of the KRAQ Knowledge and Reasoning for Answering Questions conferences (respectively at IJCAI 2005, COLING 2008 and ACL 2009), of the ACM SIGKDD Workshop on Cybersecurity and Intelligence Informatics (KDD 2009), and the Cross-media Information Access and Mining workshop (IJCAI-AAAI 2009). She is currently appointed as chair-elect of the European Chapter of the Association for Computational Linguistics (2009-2010).
Associate Professor
Department of Computer Science
Katholieke Universiteit Leuven

Presentation Slides

Demetri Terzopoulos

Artificial Life Simulation of Humans and Lower Animals: From Biomechanics to Intelligence

ABSTRACT: The confluence of virtual reality and artificial life, an emerging discipline that spans the computational and biological sciences, has yielded synthetic worlds inhabited by realistic artificial flora and fauna. The latter are complex synthetic organisms with functional, biomechanically-simulated bodies, sensors, and brains with locomotion, perception, behavior, learning, and cognition centers. These biomimetic autonomous agents in their realistic virtual worlds foster deeper computationally-oriented insights into natural living systems. Virtual humans and lower animals are of great interest in computer graphics because they are self-animating graphical characters poised to dramatically advance the motion picture and interactive game industries. Furthermore, they engender interesting new applications in computer vision, medical imaging, sensor networks, archaeology, and many other domains.

BIO: Demetri Terzopoulos (PhD '84 MIT) is the Chancellor's Professor of Computer Science at UCLA. He is a Guggenheim Fellow, a Fellow of the ACM, a Fellow of the IEEE, a Fellow of the Royal Society of Canada, and a member of the European Academy of Sciences. One of the most highly cited authors in engineering and computer science, his numerous awards include an Academy Award for Technical Achievement from the Academy of Motion Picture Arts and Sciences for his pioneering research on physics-based computer animation, and the inaugural Computer Vision Significant Researcher Award from the IEEE for his pioneering and sustained research on deformable models and their applications.
The Chancellor's Professor of Computer Science
University of California, Los Angeles
Henry Samueli School of Engineering and Applied Science Computer Science Department 4531G Boelter Hall Los Angeles, CA 90095-1596, USA