{"id":11,"date":"2009-06-08T14:05:51","date_gmt":"2009-06-08T12:05:51","guid":{"rendered":"http:\/\/agi-conf.org\/2010\/site\/?page_id=11"},"modified":"2011-09-07T06:34:48","modified_gmt":"2011-09-07T14:34:48","slug":"call-for-papers","status":"publish","type":"page","link":"https:\/\/agi-conf.org\/2011\/call-for-papers\/","title":{"rendered":"Papers"},"content":{"rendered":"

The original goal of the AI field was the construction of \u201cthinking machines\u201d, that is, computer systems with human-like general intelligence. For the last few decades, however, the majority of AI researchers have focused on what can be called \u201cnarrow AI\u201d \u2013 systems with intelligence limited to specific, highly constrained tasks. In recent years more and more researchers have recognized the necessity \u2013 and feasibility \u2013 of returning to the original goals of the field. Increasingly, there is a call for confronting the more difficult issues of human-level intelligence, and more broadly \u201cartificial general intelligence\u201d (AGI).<\/p>\n

Continuing the mission of the first three highly successful Conferences on Artificial General Intelligence, AGI-11 will gather an international group of leading academic and industry researchers involved in serious scientific and engineering work aimed directly at the goal of AGI. This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and beyond. \u00a0AGI-11 will be hosted by Google in Mountain View, California.<\/p>\n

Submit papers or proposals, for workshops, tutorials, or demos, electronically to EasyChair<\/a> (you may need to Create an EasyChair Account first). Whether an accepted paper (either full-length or short position statement) will be presented as a talk or as a poster will be determined by the Program Committee, in part based on paper quality as assessed by the anonymous reviewers, and in part according to the extent the paper addresses a topic of core interest to the AGI community. All accepted papers are required to have at least one registered author per paper. Multiple papers require multiple registrations. AGI-11 will accept two types of submissions: full-length papers (10 pages) and short position statements (3 pages).<\/p>\n

A good sense of the overall nature of the conference may be found via perusing\u00a0AGI-10<\/a>,\u00a0AGI-09<\/a>, and\u00a0AGI-08<\/a>.<\/p>\n

AGI-11 Notifications Sent!<\/span><\/h2>\n

Notifications have now been sent for all of the 100+ AGI-11 paper\u00a0submissions. Thank you to everyone who participated – there were many\u00a0excellent submissions this year. Acceptance rates were 27% for full\u00a0papers, and 52% including poster papers. If you submitted a paper and not yet received notification, please\u00a0contact us immediately. Accepted authors, please don’t forget to format your final submissions\u00a0in accordance with the guidelines<\/a>. A\u00a0prefilled copyright form for AGI-11 may be found at\u00a0Copyright Form<\/a>.<\/p>\n

Accepted Papers<\/h2>\n\n\n<\/col>\n<\/col>\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Jianglong Nan and Fintan Costello<\/td>\nA Demonstration of Combining Spatial and Temporal Perception<\/td>\n<\/tr>\n
Bas Steunebrink and J\u00fcrgen Schmidhuber<\/td>\nA Family of G\u00f6del Machine Implementations<\/a><\/td>\n<\/tr>\n
Joscha Bach<\/td>\nA Motivational System for Cognitive AI<\/a><\/td>\n<\/tr>\n
Randal Koene<\/td>\nAGI and Neuroscience: Open Sourcing the Brain<\/a><\/td>\n<\/tr>\n
Andras Lorincz and Daniel Takacs<\/td>\nAGI Architecture Measures Human Parameters and Optimizes Human Performance<\/a><\/td>\n<\/tr>\n
Samuel Epstein and Margrit Betke<\/td>\nAn Information Theoretic Representation of Agent Dynamics as Set Intersections<\/a><\/td>\n<\/tr>\n
Andrew Coward<\/td>\nBrain anatomy and artificial intelligence<\/td>\n<\/tr>\n
Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano and Juergen Schmidhuber<\/td>\nCoherence Progress: A Measure of Interestingness Based on Fixed Compressors<\/a><\/td>\n<\/tr>\n
Javier Insa-Cabrera, David L. Dowe, Sergio Espa\u00f1a-Cubillo, M.Victoria Hernandez-Lloreda and Jose Hernandez-Orallo<\/td>\nComparing humans and AI agents<\/a><\/td>\n<\/tr>\n
Eliezer Yudkowsky<\/td>\nComplex Value Systems are Required to Realize Valuable Futures<\/a><\/td>\n<\/tr>\n
David L. Dowe, Jose Hernandez-Orallo and Paramjit K. Das<\/td>\nCompression and intelligence: social environments and communication<\/a><\/td>\n<\/tr>\n
Serge Thill<\/td>\nConsiderations for a neuroscience-inspired approach to the design of artificial intelligent systems<\/td>\n<\/tr>\n
Mark Ring and Laurent Orseau<\/td>\nDelusion, Survival, and Intelligent Agents<\/a><\/td>\n<\/tr>\n
Alessandro Oltramari and Christian Lebiere<\/td>\nExtending Cognitive Architectures with Semantic Resources<\/a><\/a><\/td>\n<\/tr>\n
Paul Rosenbloom<\/td>\nFrom Memory to Problem Solving: Mechanism Reuse in a Graphical Cognitive Architecture<\/a><\/td>\n<\/tr>\n
Marius Raab, Mark Wernsdorfer, Emanuel Kitzelmann and Ute Schmid<\/td>\nFrom Sensorimotor Maps to Rules: An Agent Learns from a Stream of Experience<\/a><\/td>\n<\/tr>\n
Brian Mingus, Trent Kriete, Seth Herd, Dean Wyatte, Kenneth Latimer and Randy O’Reilly<\/td>\nGeneralization of Figure-Ground Segmentation from Binocular to Monocular Vision in an Embodied Biological Brain Model<\/a><\/td>\n<\/tr>\n
Ben Goertzel<\/td>\nImprecise Probability as a Linking Mechanism Between Deep Learning, Symbolic Cognition and Local Feature Detection in Vision Processing<\/a><\/td>\n<\/tr>\n
Pedro Ortega and Daniel Braun<\/td>\nInformation, Utility & Bounded Rationality<\/a><\/td>\n<\/tr>\n
Unmesh Kurup, Christian Lebiere and Anthony Stentz<\/td>\nIntegrating Perception and Action for AGI<\/a><\/td>\n<\/tr>\n
Haris Dindo, Antonio Chella, Giuseppe La Tona, Monica Vitali, Eric Nivel and Kristinn R. Thorisson<\/td>\nLearning Problem Solving Skills from Demonstration: An Architectural Approach<\/a><\/td>\n<\/tr>\n
Andras Lorincz<\/td>\nLearning the States of Markov Decision Processes: A Brain Inspired Neural Model<\/a><\/td>\n<\/tr>\n
Daniel Dewey<\/td>\nLearning What to Value<\/a><\/td>\n<\/tr>\n
Daniel Silver<\/td>\nMachine Lifelong Learning: Challenges and Benefits for Artificial General Intelligence<\/a><\/td>\n<\/tr>\n
Bill Hibbard<\/td>\nMeasuring Agent Intelligence via Hierarchies of Environments<\/a><\/td>\n<\/tr>\n
Karol Wal\u0119dzik and Jacek Ma\u0144dziuk<\/td>\nMultigame playing by means of UCT enhanced with automatically generated evaluation functions<\/a><\/td>\n<\/tr>\n
Matthew Ikle and Ben Goertzel<\/td>\nNonlinear-Dynamical Attention Allocation via Information Geometry<\/a><\/td>\n<\/tr>\n
Juergen Schmidhuber, Dan Ciresan, Ueli Meier, Jonathan Masci and Alex Graves<\/td>\nOn Fast Deep Nets for AGI Vision<\/td>\n<\/tr>\n
Jose Hernandez-Orallo, David L. Dowe, Sergio Espa\u00f1a-Cubillo, M.Victoria Hernandez-Lloreda and Javier Insa-Cabrera<\/td>\nOn more realistic environment distributions for defining, evaluating and developing intelligence <\/a><\/td>\n<\/tr>\n
Zhenhua Cai, Ben Goertzel and Nil Geisweiller<\/td>\nOpenPsi: Realizing Dorner\u2019s \u201dPsi\u201d Cognitive Model in the OpenCog Integrative AGI Architecture<\/a><\/td>\n<\/tr>\n
Tobias Glasmachers and J\u00fcrgen Schmidhuber<\/td>\nOptimal Direct Policy Search<\/td>\n<\/tr>\n
Iris Oved and Ian Fasel<\/td>\nPhilosophically Inspired Concept Acquisition for Artificial General Intelligence<\/td>\n<\/tr>\n
Yi Sun, Faustino Gomez and Juergen Schmidhuber<\/td>\nPlanning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments<\/td>\n<\/tr>\n
Mark Waser<\/td>\nRational Universal Benevolence: Simpler, Safer, and Wiser than \u201cFriendly AI\u201d<\/a><\/td>\n<\/tr>\n
Helmar Gust, Ulf Krumnack, Maricarmen Martinez, Ahmed Abdel-Fattah, Martin Schmidt and Kai-Uwe Kuehnberger<\/td>\nRationality and General Intelligence<\/td>\n<\/tr>\n
Leo Pape and Arthur Kok<\/td>\nReal-world Limits to Algorithmic Intelligence<\/a><\/td>\n<\/tr>\n
Pei Wang and Seemal Awan<\/td>\nReasoning in Non-Axiomatic Logic: A Case Study in Medical Diagnosis<\/a><\/td>\n<\/tr>\n
Pedro Alejandro Ortega, Daniel Alexander Braun and Simon Godsill<\/td>\nReinforcement Learning and the Bayesian Control Rule<\/a><\/td>\n<\/tr>\n
Laurent Orseau and Mark Ring<\/td>\nSelf-Modification and Mortality in Artificial Agents<\/a><\/td>\n<\/tr>\n
Linus Gisslen, Matt Luciw, Vincent Graziano and J\u00fcrgen Schmidhuber<\/td>\nSequential Constant Size Compressors and Reinforcement Learning<\/a><\/td>\n<\/tr>\n
Bill Hibbard<\/td>\nSocieties of Intelligent Agents<\/a><\/td>\n<\/tr>\n
Sergio Pissanetzky<\/td>\nStructural Emergence in Partially Ordered Sets is the Key to Intelligence<\/a><\/td>\n<\/tr>\n
Derek Monner and James Reggia<\/td>\nSystematically Grounding Language through Vision in a Deep, Recurrent Neural Network<\/a><\/td>\n<\/tr>\n
Eray Ozkural<\/td>\nTeraflop-scale Incremental Machine Learning<\/td>\n<\/tr>\n
Benjamin Johnston<\/td>\nThe Collection of Physical Knowledge and its Application in Intelligent Systems<\/a><\/td>\n<\/tr>\n
Claude Touzet<\/td>\nThe Illusion of Internal Joy<\/a><\/td>\n<\/tr>\n
Javier Snaider, Ryan Mccall and Stan Franklin<\/td>\nThe LIDA Framework as a General Tool for AGI<\/a><\/td>\n<\/tr>\n
Ben Goertzel and Matthew Ikle<\/td>\nThree Hypotheses About the Geometry of Mind<\/a><\/td>\n<\/tr>\n
Elias Ruiz, Augusto Melendez and Enrique Sucar<\/td>\nTowards a General Vision System based on Symbol-Relation Grammars and Bayesian Networks<\/a><\/td>\n<\/tr>\n
Florin Popescu<\/td>\nWagging the dog: human vs. machine inference of causality in visual sequences<\/td>\n<\/tr>\n
Janelle Szary, Bryan Kerster and Christopher Kello<\/td>\nWhat Makes a Brain Smart? Reservoir Computing as an Approach for General Intelligence<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Author Instructions<\/span><\/h2>\n

Submissions should follow the Spring Lecture Notes in Artificial Intelligence author instructions here<\/a>. \u00a0The MS Word 2007 templates may be downloaded here<\/a> also.<\/p>\n

Important Dates<\/span><\/h2>\n

Final Submissions – Due 1st of March (extended from 15th of February), 2011 Notification Date – Due 26th of April<\/p>\n

<\/p>\n

AGI-11 Submissions Policy<\/span><\/h2>\n

Whether an accepted paper (of either length) will be presented as a talk or as a poster will be determined by the Program Committee, in part based on paper quality as assessed by the anonymous reviewers, and in part according to the extent the paper addresses a topic of core interest to the AGI community. The acceptance of a paper is based on the assumption that one of the authors will attend the conference to present the paper. Any questions can be directed to one of the\u00a0conference chairs<\/a>. <\/a><\/p>\n

AGI-11 Copyright Policy<\/span><\/h2>\n

Springer-Verlag will hold the copyright to the published paper. Authors should archive their paper on their own web site as well, with the following text included: \u201cThe original publication is available at www.springerlink.com\u201d. The Springer Lecture Notes in Artificial Intelligence copyright form is available\u00a0here<\/a>. \"\"<\/p>\n

The AGI conference will be part of Springer Lecture Notes in Artificial Intelligence<\/a> (LNAI). This subseries is devoted to the publication of state-of-the-art research results in artificial intelligence, at a high level and in both printed and electronic versions. The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.<\/p>\n","protected":false},"excerpt":{"rendered":"

The original goal of the AI field was the construction of \u201cthinking machines\u201d, that is, computer systems with human-like general intelligence. For the last few decades, however, the majority of AI researchers have focused on what can be called \u201cnarrow AI\u201d \u2013 systems with intelligence limited to specific, highly constrained tasks. In recent years more […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":165,"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":131,"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/pages\/11\/revisions\/131"}],"wp:attachment":[{"href":"https:\/\/agi-conf.org\/2011\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}