-DARPA backed Siri Nearing Launch Of ‘Personal’ Artificial Intelligence.

Posted: October 15, 2008 in 2008, Articles
Tags: ,

c|net (snippets):

In the midst of the financial meltdown and a contentious upcoming election, you might think the U.S. government and taxpayers are just funding wars, bank bailouts, and bridges to nowhere or somewhere. But this is the same government that funded the Internet way back when and is also funding the next generation of technologies that will make the current Internet seem like a Model-T.

Over the last several years, the U.S. government–via DARPA (Defense Advanced Research Projects Agency) grants–has invested hundreds of millions of dollars in PAL, an acronym for “Personalized Assistant that Learns.” Smarter software and networks and augmenting human intelligence are useful in times of war and peace.

As part of the PAL project, more than $200 million of DARPA money has been poured into CALO (Cognitive Assistant that Learns and Organizes) over the last five years. CALO has been run out of SRI International with the assistance of 25 research organizations and 400 researchers.

Several companies, including Radar Networks, Farecast (acquired by Microsoft) and Adapx, have been spun out of SRI based on some facet of CALO technology. The latest, Siri, was founded in December last year and has raised $8.5 million in series A funding from Menlo Ventures and Morgenthaler Ventures.

He also touted the pedigree of the company’s current cadre of 19 employees. “They are mostly engineers from Yahoo, Google, SRI, NASA, and Xerox PARC,” he said. The chief architect of the CALO project, Adam Cheyer is a co-founder and vice president of engineering at Siri, and Tom Gruber, a well-known artificial intelligence and semantic Web expert, is a co-founder and CTO.

Venture Beat (snippet):

Conspiracy theorists will love this one: A computerized assistant that can help you manage your day to day life, built atop an artificial intelligence platform developed by the Defense Advanced Research Projects Agency (DARPA), the United States’ internal military research group. Siri, the startup building the assistant, is today announcing $8.5 million in venture funding.

As befits its spookish origins, Siri isn’t saying a great deal yet about what it will do. Co-founder Dag Kittlaus, who licensed technology from DARPA’s CALO (Cognitive Agent that Learns and Organizes) project, calls it “a smarter, more personal interaction paradigm for the Internet.” Unfortunately, that’s about as specific as calling Google “a thing that finds stuff.” Those who want a sneak peek at Siri will instead have to look to CALO.

So here’s what we know about CALO: It’s a concerted effort to take the first real step toward artificial intelligence, with five years of work and $200 million in funding to date. Rather than being immediately useful, it learns about the user over time, much like a real personal assistant would. As it learns, it becomes capable of making logical associations and initiating its own actions.

Obviously, DARPA didn’t start the project to help officers plan out their vacation retreats. Internally, it’s meant to help with tasks like running a platoon of soldiers, and actual development has been centered around enterprise usage, according to SemanticWeb.com. Siri is likely just the first of several startups we’ll see emerge to try to reach a broader market with the technology.

Read Write Web (snippets):

We got a chance to talk to Siri’s co-founders Dag Kittlaus and Adam Cheyer today. Both Dag Kittlaus, who is the company’s CEO, and Adam Cheyer, Siri’s VP of Engineering, bring an impressive background of experience in the mobile industry and artificial intelligence research to the table. The third co-founder of Siri is Tom Gruber, a well-known expert on artificial intelligence and interface design. Siri’s 19-person team has been recruited from companies such as Google, SRI, NASA, Xerox PARC, Motorola, and Apple.

  • Goal: Siri wants to change the ‘personal interaction paradigm’ for the internet. Tom Gruber has talked about the need for this at length during a talk at SemTech 2008 earlier this year. In this talk, Gruber focuses on bringing ‘intelligence to the interface’ and creating products that are personalized and context-aware. Judging from this and the work of the CALO project, we expect Siri to have a strong information management aspect, combined with some novel interface ideas.
  • Mobile: Based on our discussion with Dag Kittlaus and Adam Cheyer, we think that there will be a strong mobile aspect to Siri’s product and at least some emphasis on location awareness. Siri’s beta signup page seems to confirm this suspicion.
  • Partners: Siri currently has 12 hardware and software partners, all of which would be “names you already know.”
  • Launch: Siri is planning to release a public version of its product in the first half of 2009.

Siri:

Siri represents a new interaction paradigm for the consumer internet experience.

Siri is born from the largest Artificial Intelligence project in U.S. history. Years in the making, Siri is being prepared for the Internet by a great team. Siri will be available in a public beta in the first half of 2009.

Be one of the first to experience Siri, sign up for our Beta release.

CALO Project:

SRI International is leading the development of new software that could revolutionize how computers support decision-makers.

The Defense Advanced Research Projects Agency (DARPA), under its Personalized Assistant that Learns (PALdownload brochure) program, has awarded SRI three phases of a five-year contract to develop an enduring personalized cognitive assistant. DARPA expects the PAL program to generate innovative ideas that result in new science, new approaches to current problems, new algorithms and tools, as well as new technology of significant value to the military.

The team dubbed its new project CALO, for Cognitive Assistant that Learns and Organizes. The name was inspired by the Latin word “calonis,” which means “soldier’s servant.” The goal of the project is to create cognitive software systems, that is, systems that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise.

The software, which learns by interacting with and being advised by its users, will handle a broad range of interrelated decision-making tasks that have in the past been resistant to automation. A CALO will have the capability to engage in and lead routine tasks, and to assist when the unexpected happens. To focus the research on real problems and ensure the software meets requirements such as privacy, security, and trust, the CALO project researchers themselves are using the technology during its development.

SRI is leading the multi-disciplinary CALO project team and, beyond participating in the research program, is also responsible for overall project direction, management, and development of prototypes. The project is bringing together leading computer scientists and researchers in artificial intelligence, machine learning, natural language processing, knowledge representation, human-computer interaction, flexible planning, and behavioral studies.

Mashable (snippets):

The company is called Siri, and it’s so serious about it’s stealth status that it’s even registered the domain stealth-company.com. We chatted today over the fact that they’ve announced their $8.5 million Series A with Morgenthaler and Menlo Ventures.

DARPA gave the research outfit SRI International the contract to work on the CALO Project, which was commissioned with coming up with PAL. Word soup enough for you yet? CALO stands for “Cognitive Assistant that Learns and Organizes” and PAL stands for “Personalized Assistant that Learns.” The original applications were meant to be military in nature (CALO was inspired by the Latin root “calonis” which means “soldier’s servant), but as Dag explained today, the enterprise applications are pretty obvious.

These were the sorts of technical hurdles that CALO faced when they were assigned $200 million and a mandate to make AI work. These issues have been present in every AI that’s attempted to tackle the personal assistant problem for the last thirty years. There have been one or two exceptions, but no breakout stars have emerged into the public eye.

Some of the folks have been exposed to it have called Siri a search engine, but it really promises to be a whole lot more.  The way people interact with the Internet is still very highly manual, and Siri is an attempt to create an automated way of going at it.

So much of what we have in Web 2.0 and social media is waiting to be used in this way. Every tool we interact with daily has an API, and we’ve been manually hooking them into just about every service and social network we’ve signed up for in the last six months. Back last November, I jokingly suggested some potential definitions to the successor technology for Web 2.0, and referenced Eric Schmidt and Ken Rutkowski’s Web 3.0 definition: “applications that are pieced together.”

Chances are, you don’t need yet another search engine to go out and find content on the web. You need bridges between your own content and communications. You need your calendar to be aware immediately when you schedule a meeting via your email. If there’s a scheduling conflict, it wouldn’t be completely unwelcome if your personal assistant contacted you and the other participants to find a common time that works for everyone.

There are literally hundreds of applications along these lines that an intelligent learning agent can touch points of interaction in our lives and make the mundane parts of the day disappear. I’m waiting with baited breath to see exactly how ambitious Siri intends to be in tackling these issues it’ parent projects set out to do.

About CALO:

The Defense Advanced Research Projects Agency (DARPA) has awarded SRI three years of a five-year contract to develop an enduring Personalized Assistant that Learns (PAL). The program responds to DARPA’s New Cognitive Systems Vision, which states that “A cognitive computer system should be able to learn from its experience, as well as by being advised. It should be able to explain what it was doing and why it was doing it, and to recover from mental blind alleys. It should be able to reflect on what goes wrong when an anomaly occurs, and anticipate such occurrences in the future. It should be able to reconfigure itself in response to environmental changes. And it should be able to be configured, maintained, and operated by non-experts.”

DARPA expects the PAL program to generate innovative ideas that result in new science, new approaches to current software problems, new algorithms and tools, and new technology of significant value to the military. CALO is one of two projects funded by PAL. The other is RADAR.

CALO stands for Cognitive Assistant that Learns and Organizes. The name was inspired by the Latin word calonis, “soldier’s servant,” because DARPA’s goal is to create a cognitive system that can reason, learn, and respond to surprise in order to assist in military situations.

The CALO project brings together leading computer scientists and researchers in artificial intelligence, perception, machine learning, natural language processing, knowledge representation, multimodal dialog, cyber-awareness, human-computer interaction, and flexible planning. The single research focus of all these experts is to create an integrated system that can “learn in the wild”—that is, adapt to changes in its environment and its user’s goals and tasks without programming assistance or technical intervention. The groundbreaking nature of this ambitious goal is discussed further in the CALO Vision.

Related Links

DARPA and CALO:

Within DARPA are twelve offices conducting advanced scientific and engineering research. Each office sponsors a variety of programs. The Information Processing Technologies Office (IPTO) sponsors the CALO and RADAR projects as part of its Personalized Assistant that Learns (PAL) program. While SRI International in Menlo Park, California, administrates both the CALO and RADAR portions of the PAL project, this Web site is dedicated solely to CALO.

In addition to managing the multidisciplinary CALO project, SRI is home to the Integration Team, which is responsible for creating the CALO platform and for integrating the disparate research efforts into a single, fully functional system. This single system is the ultimate goal of the CALO project.

For more information about the PAL Program and IPTO’s vision of artificial intelligence in defense applications, visit IPTO’s PAL Program Page

See the CALO Web site’s Related Efforts to learn about RADAR and other activities, including projects outside the CALO community, that may inform CALO development.

Relevant Links

CALO Presentation at DARPATech 2005
DARPATech 2005 PAL Brochure

Related Research Effort:

RADAR
RADAR, the Reflective Agent with Distributed Adaptive Reasoning, is a $7 million dollar, five-year research project in Carnegie Mellon University’s School of Computer Science. The overall goal is to develop a software-based “cognitive personal assistant” that will help busy military commanders and managers to work more effectively, with less time wasted on routine tasks. This new technology should be equally valuable to managers in industry, academia, and government. RADAR is funded by the Information Processing Technology Office (IPTO) of DARPA and managed by SRI International.

IRIS

IRIS is a semantic desktop application framework that enables users to create a “personal map” across their office-related information objects. IRIS includes a machine-learning platform to help automate this process. It provides “dashboard” views, contextual navigation, and relationship-based structure across an extensible suite of office applications, including a calendar, Web and file browser, e-mail client, and instant messaging client.

CALO Project Team:

Boeing Phantom WorksCarnegie Mellon University

CollaborX Inc.

Fetch Technologies, Inc.

Georgia Tech Research Corporation

Harvard University

iAnyWhere Solutions

International Computer Science Institute (ICSI)

ISX Corporation

Laszlo Systems

Massachusetts Institute of Technology

Natural Interaction Systems, LLC

Oregon Health & Science University

Oregon State University

PARC

Radar NetworksSRI International

Stanford University

State University of New York at Stony Brook

University of California at Berkeley

University of California at Santa Cruz

University of Massachusetts at Amherst

University of Michigan

University of Pennsylvania

University of Rochester

University of Southern California

USC Information Sciences Institute

University of Texas at Austin

University of Washington

University of West Florida: Institute of Human and Machine Cognition

Yale University

CALO Published Research:

A* Based Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings, Matthias Zimmermann, Yang Liu, Elizabeth Shriberg, and Andreas Stolcke. Proceedings of the IEEE Speech Recognition and Understanding Workshop, Cancun, 2005.

Active Preference Learning for Personalized Calendar Scheduling Assistance, Melinda T. Gervasio, Michael D. Moffitt, Martha E. Pollack, Joseph M. Taylor, and Tomas E. Uribe. Proceedings of the 2005 International Conference on Intelligent User Interfaces, 2005.

Activity recognition and abnormality detection with the switching hidden semi-Markov model, T. Duong, H. Bui, D. Phung, and S. Vekatesh. IEEE International Conference on Computer Vision and Pattern Recognition, 2005.

Adjustable Autonomy Challenges in Personal Assistant Agents: A Position Paper, R. Maheswaran, M. Tambe, P. Varakantham, and K. Myers. The First International Workshop on Computational Autonomy – Potential, Risks, Solutions (Autonomy 2003), 2003.

Analysis of Overlaps in Meetings by Dialog Factors, Hot Spots, Speakers, and Collection Site: Insights for Automatic Speech Recognition, Ozgur Cetin and Elizabeth Shriberg (2006). Proceedings of ICSLP, pp. 293-296, Pittsburgh.

Analysis of Privacy Loss in Distributed Constraint Optimization, Rachel Greenstadt, Jonathan P. Pearce and Milind Tambe. Copyright 2006, American Association for Artificial Intelligence.

Asimovian Multiagents: Applying Laws of Robotics to Teams of Humans and Agents, Nathan Schurr, Pradeep Varakantham, Emma Bowring, Milind Tambe, and Barbara Grosz.

Balancing Formal and Practical Concerns in Agent Design, David Morley and Karen Myers. Proceedings of AAAI Workshop on Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems, 2004.

Bibliometric Impact Measures Leveraging Topic Analysis, Gideon S. Mann, David Mimno, and Andrew McCallum. JCDL’06, June 11–15, 2006, Chapel Hill, North Carolina, USA.

Building an Intelligent Personal Assistant, Karen Myers. AAAI Invited Talk, July 2006.

Can Modeling Redundancy In Multimodal, Multi-party Tasks Support Dynamic Learning? Edward C. Kaiser. CHI 2005 Workshop: CHI Virtuality 2005, Portland, OR., USA, April 3, 2005.

Balancing Formal and Practical Concerns in Agent Design, David Morley and Karen Myers. In Proceedings of AAAI Workshop on Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems, 2004.

A Case Study in Engineering a Knowledge Base for an Intelligent Personal Assistant, Vinay K. Chaudhri, Adam Cheyer, Richard Guili, Bill Jarrold, Karen Myers, and John Niekarasz. Technical Report. SRI International, 2006.

A Cognitive Framework for Delegation to an Assistive User Agent, K. Myers and N. Yorke-Smith. Proceedings of AAAI 2005 Fall Symposium on Mixed-Initiative Problem Solving Assistants, Arlington, VA, November 2005.

Collaborative Multimodal Photo Annotation over Digital Paper, Paulo Barthelmess, Edward Kaiser, Xiao Huang, David McGee, and Philip Cohen. ICMI’06, November 2–4, 2006; Banff, Alberta, Canada.

Collective Multi-Label Classification, Nadia Ghamrawi and Andrew McCallum. CIKM’05, Bremen, Germany.

Combining User Modeling and Machine Learning to Predict Users’ Multimodal Integration Patterns, Xiao Huang, Sharon Oviatt, and Rebecca Lunsford. Technical paper by Natural Interaction Systems and Center for Human-Computer Communication.

Composition of Conditional Random Fields for Transfer Learning, Charles Sutton and Andrew McCallum. Proceedings of HLT/EMNLP, 2005.

Conflict Negotiation Among Personal Calendar Agents, Pauline M. Berry, Cory Albright, Emma Bowring, Ken Conley, Kenneth Nitz, Jonathan P. Pearce, Bart Peintner, Shahin Saadati, Milind Tambe, Tomás Uribe, and Neil Yorke-Smith. Proceedings AAMAS’06, May 8-12, 2006, Hakodate, Hokkaido, Japan.

Continuous Refinement of Resource Estimates, D. N. Morley, K. L. Myers,and N. Yorke-Smith. Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS’06), Hakodate, Japan, May 2006.

A Demonstration of Distributed Pointing and Referencing for Multimodal Collaboration Over Sketched Diagrams, Edward C. Kaiser, P. Barthelmess, X. Huang and D. Demirdjian. Workshop Proceedings of the Seventh International Conference on Multimodal Interfaces (ICMI 2005), Workshop on Multimodal, Multiparty Meeting Processing, Oct. 7, 2005, Trento, Italy.

Demo: Collaborative Multimodal Photo Annotation over DigitalPaper, Paulo Barthelmess, Edward Kaiser, Xiao Huang, David McGee, and Philip Cohen. ICMI’06, November 2–4, 2006; Banff, Alberta, Canada.

Demo: A Multimodal Learning Interface for Sketch, Speak and Point Creation of a Schedule Chart, Ed Kaiser, David Demirdjian, Alexander Gruenstein, Xiaoguang Li, John Niekrasz, Matt Wesson, and Sanjeev Kumar. Proceedings of the Sixth International Conference on Multimodal Interfaces (ICMI 2004), State College, Pennsylvania, USA, October 14-15, 2004, pgs. 329-330.

Deploying a Personalized Time Management Agent, P. Berry, K. Conley, M. Gervasio, B. Peintner, T. Uribe, and N. Yorke-Smith. Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS’06) Industrial Track, Hakodate, Japan, May 2006.

Design and Implementation of the CALO Query Manager, Jose-Luis Ambite, Vinay K. Chaudhri, Richard Fikes, Jessica Jenkins, Sunil Mishra, Maria Muslea, Tomas Uribe, Guizhen Yang. Innovative Applications of Artificial Intelligence, July 2006.

Dialogue Structure and Pronoun Resolution, J. Tetreault, and J. Allen.

Disjunctive Temporal Planning with Uncertainty, K.B. Venable and N. Yorke-Smith. Proceedings of IJCAI’05, Edinburgh, UK, August 2005.

Distributed Pointing for Multimodal Collaboration over Sketched Diagrams, Paulo Barthelmess, Ed Kaiser, Xiao Huang, David Demirdjian. Proceedings of the Seventh International Conference on Multimodal Interfaces (ICMI ’05), Trento, Italy, October 4-6, 2005, pgs. 10-17.

Dynamic New Vocabulary Enrollment through Handwriting and Speech in a Multimodal Scheduling Application, Edward C. Kaiser. Making Pen-Based Interaction Intelligent and Natural, Papers from the 2004 AAAI Symposium, Technical Report FS-04-06, Arlington, VA., USA, October 21-24, 2004, pgs. 85-91.

Efficiently Ordering Subgoals with Access Constraints, Guizhen Yang, Michael Kifer, and Vinay K. Chaudhri. ACM International Symposium on Principles of Database Systems (PODS), June 2006.

Electric Elves: What Went Wrong and Why, Milind Tambe, Emma Bowring, Jonathan P. Pearce, Pradeep Varakantham, Paul Scerri, and David V. Pynadath. Copyright 2005, American Association for Artificial Intelligence.

Enriching Speech Recognition with Automatic Detection of Sentence Boundaries and Disfluencies, Y. Liu, E. Shriberg, A. Stolcke, D. Hillard, M. Ostendorf, and M. Harper (2006). IEEE Trans. Audio, Speech and Language Processing, 14(5), 1526-1540.

Experimental analysis of privacy loss in DCOP algorithms, Rachel Greenstadt, Jonathan P. Pearce, Emma Bowring, and Milind Tambe. AAMAS’06 May 8–12 2006, Hakodate, Hokkaido, Japan.

Exploiting Secondary Sources for Unsupervised Record Linkage, Martin Michalowski, Snehal Thakkar, and Craig A. Knoblock. Proceedings of the 2004 VLDB Workshop on Information Integration on the Web, 2004.

Extracting Knowledge about Users’ Activities from Raw Workstation Contents, T. Mitchell, S. Wang, Y. Huang, and A. Cheyer. The Twenty-First National Conference on Artificial Intelligence (AAAI ’06), July 2006.

Fewer Clicks and Less Frustration: Reducing the Cost of Reaching the Right Folder, X. Bao, J.Herlocker, and T. Dietterich. 2006 International Conference on Intelligent User Interfaces. 178-185. Sydney, Australia.

Group and Topic Discovery from Relations and Text, Xuerui Wang, Natasha Mohanty, and Andrew McCallum. LinkKDD2005 August 21, 2005, Chicago, Illinois, USA.

Hierarchical Hidden Markov Models with General State Hierarchy, H. Bui, D. Phung, and S. Venkatesh. Proceedings of AAAI, 2004.

Human-Centered Collaborative Interaction. Paulo Barthelmess, Edward Kaiser, Rebecca Lunsford, David McGee, Philip Cohen,and Sharon Oviatt. HCM’06, October 27, 2006; Santa Barbara, California.

A Hybrid Learning System for Recognizing User Tasks from Desktop Activities and Email Messages, J. Shen, L. Li, T. Dietterich, and J. Herlocker. 2006 International Conference on Intelligent User Interfaces, 86-92. Sydney, Australia.

The ICSI+ Multi-Lingual Sentence Segmentation System, M. Zimmermann, D. Tur, J. Fung, N. Mirghafori, L. Gottlieb, E. Shriberg, and Y. Liu (2006). Proceedings of ICSLP, pp. 117-120, Pittsburgh.

Implementation Techniques for Solving POMDPs in Personal Assistant Domains, Pradeep Varakantham, Rajiv Maheswaran, and Milind Tambe.

Incremental Parsing with Reference Interaction, S. Stoness, J. Tetreault, and J. Allen.

Integration of Heterogeneous Knowledge Sources in the CALO Query Manager, Jose-Luis Ambite1, Vinay K. Chaudhri, Richard Fikes, Jessica Jenkins, Sunil Mishra, Maria Muslea, Tomas Uribe, and Guizhen Yang. SRI Technical Report, August 2005.

An Introduction to Conditional Random Fields for Relational Learning, Charles Sutton and Andrew McCallum.

IRIS: Integrate. Relate. Infer. Share. Adam Cheyer, Jack Park, and Richard Giuli. Workshop on The Semantic Desktop – Next Generation Personal Information Management and Collaboration Infrastructure at the International Semantic Web Conference (ISWC2005). 6 November 2005, Galway, Ireland.

Introduction to SPARK, D. Morley.

Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Model, N. Nguyen, D. Phung, S. Venkatesh, and H. Bui. In IEEE International Conference on Computer Vision and Pattern Recognition, 2005.

Joint Deduplication of Multiple Record Types in Relational Data, Aron Culotta and Andrew McCallum. CIKM’05, Bremen, Germany.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model, N. Nguyen, D. Phung, S. Venkatesh, and H. Bui. IEEE International Conference on Computer Vision and Pattern Recognition, 2005.

Managing Extrinsic Costs via Multimodal Natural Interaction Systems, Rebecca Lunsford, Ed Kaiser, Paulo Barthelmess, and Xiao Huang. CHI 2006, April 22–28, 2006, Montréal, Québec, Canada.

Mixed-Initiative Issues for a Personalized Time Management Assistant, P. Berry, M. Gervasio, T. Uribe, and N. Yorke-Smith. Proceedings of ICAPS’05 Workshop on Mixed-Initiative Planning and Scheduling, Monterey, CA, pp. 12-17, Jun 2005.

More Than Words Can Say: Using Prosody to Find Sentence Boundaries in Speech, Y. Liu and E. Shriberg (2006). 4th ASA/ASJ Joint Meeting Lay Language Papers. Popular version of paper IaSC2, 4th ASA/ASJ Joint Meeting, Honolulu, HI.

Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification, Andrew McCallum, Chris Pal, Greg Druck, and Xuerui Wang. AAAI, 2006.

Multi-Criteria Evaluation in User-Centric Distributed Scheduling Agents, P.M. Berry, M. Gervasio, B. Peintner, T. Uribe, and N. Yorke-Smith. AAAI Spring Symposium on Distributed Plan and Schedule Management, Mar 2006.

Multimodal New Vocabulary Recognition through Speech and Handwriting in a Whiteboard Scheduling Application, Edward C. Kaiser. Proceedings of the International Conference on Intelligent User Interfaces, San Diego, CA., January 9-12, 2005, pgs. 51-58.

Multimodal Play Back of Collaborative Multiparty Corpora, Edward C. Kaiser, P. Barthelmess, Alexander Arthur. Workshop Proceedings of the Seventh International Conference on Multimodal Interfaces (ICMI 2005), Oct. 7, 2005, Trento, Italy.

Multiply-Constrained Distributed Constraint Optimization, E. Bowring, M. Tambe, and M. Yokoo. AAMAS’06 May 8–12 2006, Hakodate, Hokkaido, Japan.

Multiply-Constrained DCOP for Distributed Planning and Scheduling, E. Bowring, M. Tambe, and M. Yokoo. Copyright 2006, American Association for Artificial Intelligence.

Mutual Disambiguation of 3D Multimodal Interaction in Augmented and Virtual Reality, Ed Kaiser, Alex Olwal, David McGee, Hrvoje Benko, Andrea Corradini, Xiaoguang Li, Phil Cohen, and Steven Feiner. Proceedings of the 5th International Conference on Multimodal Interfaces (ICMI 2003), Vancouver, B.C., Canada, November 2003, pgs. 12-19.

Online Query Relaxation via Bayesian Causal Structures Discovery, Ion Muslea and Thomas J. Lee. Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI 2005), Pittsburgh, Pennsylvania, 2005.

Mixed-Initiative Issues for a Personalized Time Management Assistant, P. Berry, M. Gervasio, T. Uribe, and N. Yorke-Smith. In Proceedings of ICAPS’05 Workshop on Mixed-Initiative Planning And Scheduling, Monterey, CA, June 2005.

On Speaker-Specific Prosodic Models for Automatic Dialog Act Segmentation of Multi-Party Meetings, J. Kolar, E. Shriberg, Y. Liu (2006). Proceeding of ICSLP, pp. 2014-2017, Pittsburgh.

Overlap in Meetings: ASR Effects and Analysis by Dialog Factors, Speakers, and Collection Site, Ozgur Cetin and Elizabeth Shriberg. Proceedings of MLMI06 (3rd Joint Workshop on Multimodal and Related Machine Learning Algorithms), Washington DC.

A Personalized Calendar Assistant, Pauline M. Berry, Melinda Gervasio, Tomas Uribe, Karen Myers, and Ken Nitz. Proceedings of the AAAI Spring Symposium Series, Stanford University, March 2004.

A Personalized Time Management Assistant, Pauline M. Berry, Melinda Gervasio, Tomas Uribe, Martha Pollack, and Michael Moffitt. Proceedings of the AAAI 2005 Spring Symposium Series, Stanford, CA, March 2005.

A Personalized Time Management Assistant, Pauline M. Berry, Melinda Gervasio, Tomas Uribe, Martha Pollack, and Michael Moffitt. In Proceedings of the AAAI 2005 Spring Symposium Series, Stanford, CA, March 2005.

Populating the Semantic Web, Kristina Lerman, Cenk Gazen, Steven Minton, and Craig A. Knoblock. Proceedings of the AAAI 2004 Workshop on Advances in Text Extraction and Mining, 2004.

A Portable Process Language, Peter E. Clark, David Morley, Vinay K. Chaudhri, and Karen L. Myers. In Workshop on the Role of Ontologies in Planning and Scheduling, Monterey, CA; June 7, 2005.

A Portable Process Language, Peter E. Clark, David Morley, Vinay K. Chaudhri, and Karen L. Myers. In Workshop on the Role of Ontologies in Planning and Scheduling, Monterey, CA; June 7, 2005.

A Probabilistic Model of Redundancy in Information Extraction, D. Downey, O. Etzioni, and S. Soderland.

Proceedings of the ISWC 2005 Workshop on The Semantic Desktop – Next Generation Information Management & Collaboration Infrastructure, Galway, Ireland, November 6, 2005, CEUR-WS.org, vol. 175, November 2005. Stefan Decker, Jack Park, Dennis Quan, Leo Sauermann (ed.)

Quality Guarantees on Locally Optimal Solutions for Distributed Constraint Optimization Problems, Jonathan P. Pearce and Milind Tambe.

Quiet Interfaces That Help Students Think, Sharon Oviatt, Alex Arthur, and Julia Cohen.

Recovery from Interruptions: Knowledge Workers? Strategies, Failures and Envisioned Solutions, Simone Stumpf, Margaret Burnett, Thomas G. Dietterich, Kevin Johnsrude, Jonathan Herlocker, and Vidya Rajaram.
Institution: Oregon State University Corvallis, OR

Retrieving and Semantically Integrating Heterogeneous Data from the Web, Martin Michalowski, José Luis Ambite, Snehal Thakkar, Rattapoom Tuchinda, Craig A. Knoblock, and Steve Minton. IEEE Intelligent Systems, 19(3), 2004.

Semantics, Dialogue, and Pronoun Resolution, J. Tetreault and J. Allen.

Semi-Supervised Text Classification Using EM, Kamal Nigam, Andrew McCallum, and Tom M. Mitchell.

SHACER: a Speech and Handwriting Recognizer, Edward C. Kaiser. Workshop Proceedings of the Seventh International Conference on Multimodal Interfaces (ICMI 2005), Workshop on Multimodal, Multiparty Meeting Processing, Oct. 7, 2005, Trento, Italy.

Skeletons in the Parser: Using Shallow Parsing to Improve Deep Parsing, M. Swift, J. Allen, and D. Gildea.

Solution Sets in DCOPs and Graphical Games, Jonathan P. Pearce, Rajiv T. Maheswaran, and Milind Tambe. AAMAS’06 May 8–12 2006, Hakodate, Hokkaido, Japan.

The SPARK Agent Framework, David Morley and, Karen Myers. In Proceedings of the Third Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS-04), New York, NY, pp. 712-719, July 2004.

The SRI Procedural Agent Realization Kit (SPARK) Agent Framework, D. Morley and K. Myers.

Task Management under Change and Uncertainty: Constraint Solving Experience with the CALO Project, P. Berry, K. Myers, T. Uribe, and N. Yorke-Smith. In Proceedings of CP’05 Workshop on Constraint Solving under Change and Uncertainty, Sitges, Spain, October 2005.

The SPARK Agent Framework, David Morley and, Karen Myers. Proceedings of the Third Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS-04), New York, NY, pp. 712-719, July 2004.

Speaker Overlaps and ASR Errors in Meetings: Effects Before, During, and After the Overlap, Ozgur Cetin and Elizabeth Shriberg. Proceedings of the IEEE ICASSP, Toulouse, 2006

Task Management under Change and Uncertainty: Constraint Solving Experience with the CALO Project, P. Berry, K. Myers, T. Uribe, and N. Yorke-Smith. Proceedings of CP’05 Workshop on Constraint Solving under Change and Uncertainty, Sitges, Spain, October 2005.

Temporal Planning with Preferences and Probabilities, R. Morris, P. Morris, Khatib, L. and N. Yorke-Smith. Proceedings of ICAPS’05 Workshop on Constraint Programming for Planning and Scheduling, Monterey, CA, June 2005.

To Transfer or Not to Transfer, M. T. Rosenstein, Z. Marx, L. P. Kaelbling, and T. G. Dietterich. NIPS 2005 Workshop on Transfer Learning, Whistler,BC.

Toward Adaptive Information Fusion in Multimodal Systems, Xiao Huang and Sharon Oviatt. Technical paper, Center for Human-Computer Communication.

Transfer Learning with an Ensemble of Background Tasks, Z. Marx, M. T. Rosenstein, L. P. Kaelbling, and T. G. Dietterich. NIPS 2005 Workshop on Transfer Learning, Whistler, BC.

Using Prosody for Automatic Sentence Segmentation of Multi-Party Meetings, J. Kolar, E. Shriberg, and Y. Liu (2006). Proceedings of International Conference on Text, Speech, and Dialogue (TSD), Czech Republic.


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