AAAI Sponsored Workshops—AAAI-16

February 12–13, 2016     Phoenix, Arizona USA     Call for Submissions

Sponsored by the Association for the Advancement of Artificial Intelligence

Important Dates for Workshop Organizers

• October 23: Submissions due (unless noted otherwise)
• November 23: Notification of acceptance
• December 7: Camera-ready copy due to AAAI
• February 12-13: AAAI-16 Workshop Program

• W1: Artificial Intelligence Applied to Assistive Technologies and Smart Environments
• W2: AI, Ethics, and Society
• W3: Artificial Intelligence for Cyber Security
• W4: AI for Smart Grids and Smart Buildings
• W5: Beyond NP
• W6: Computer Poker and Imperfect Information Games
• W7: Declarative Learning Based Programming
• W8: Expanding the Boundaries of Health Informatics Using AI
• W9: Incentives and Trust in Electronic Communities
• CANCELLED W10: Integration of Learning and Diagnosis
• W11: Knowledge Extraction from Text
• W12: Multiagent Interaction without Prior Coordination
• W13: Planning for Hybrid Systems
• W14: Scholarly Big Data: AI Perspectives, Challenges, and Ideas
• W15: Symbiotic Cognitive Systems
• W16: World Wide Web and Population Health Intelligence

Schedule
 

(All workshops are one full day.)

Friday, February 12

• W1: Artificial Intelligence Applied to Assistive Technologies and Smart Environments
• W3: Artificial Intelligence for Cyber Security
• W4: AI for Smart Grids and Smart Buildings
• W5: Beyond NP
• W9: Incentives and Trust in Electronic Communities
• CANCELLED W10: Integration of Learning and Diagnosis
• W11: Knowledge Extraction from Text
• W16: World Wide Web and Population Health Intelligence

Saturday, February 13

• W2: AI, Ethics, and Society
• W6: Computer Poker and Imperfect Information Games
• W7: Declarative Learning Based Programming
• W8: Expanding the Boundaries of Health Informatics using AI
• W12: Multiagent Interaction without Prior Coordination
• W13: Planning for Hybrid Systems
• W14: Scholarly Big Data: AI Perspectives, Challenges, and Ideas
• W15: Symbiotic Cognitive Systems

W01 — Artificial Intelligence Applied to Assistive Technologies and Smart Environments

Ambient intelligence can help, transform, and enhance the way people with disabilities perform their activities of daily living, activities that would otherwise be difficult or impossible for them to do. However, despite the increasing trend toward the development of new assistive technologies to help people with disabilities, no real adoption tendency has been observed yet, regarding the targeted user groups. Indeed, users impairments and particularities are so diverse, that implementing complex technological solutions — mandatory for user adaptation — represents a major challenge in terms of universal design. In such a context, the main objective of this workshop is to investigate new solutions to scientific problems occurring in the various topics related to artificial intelligence applied in the domain of impaired people assistance.

Topics

This workshop will explore various topics including, but not limited to the following:

• Algorithms for plan, activity, intent, or behavior recognition or prediction
• Personalization (user modeling, user profile, etc.)
• Algorithms for intelligent proactive assistance
• Context awareness
• High-level activity and event recognition
• Multi-person localization
• Autonomic computing
• High-level control of autonomous systems
• Fault tolerance of assistive technologies
• Pervasive and/or mobile cognitive assistance

Format

This one-day workshop will consist of invited talks from experts, technical and position papers presentations organized into topical sessions (decided based on submissions), and a poster session depending on the participation. To encourage discussion, the workshop will be limited to 50 invited participants.

Submissions

The organizing committee is currently seeking either technical papers up to six pages in the conference format, or else, for poster presentations, authors should submit a short paper or extended abstract, up to 2 pages describing research relevant to the workshop.

Workshop Cochairs

Bruno Bouchard
418 545-5011 (5604)
bruno.bouchard@uqac.ca
555, boul. de l'Université
Chicoutimi, QC, G7H 2B1, Canada

Abdenour Bouzouane
418 545-5011 (5214)
abdenour.bouzouane@uqac.ca
555, boul. de l'Université
Chicoutimi, QC, G7H 2B1, Canada

Sébastien Gaboury
418 545-5011 (2604)
Sebastien.Gaboury@uqac.ca
555, boul. de l'Université
Chicoutimi, QC, G7H 2B1, Canada

Sylvain Giroux
819 821-8000 (62027)
sylvain.giroux@usherbrooke.ca
Université de Sherbrooke,
2500, boul. de l'Université,
Sherbrooke, QC, J1K 2R1, Canada

Sébastien Guillet
418 545-5011 (5063)
sebastien.guillet1@uqac.ca
555, boul. de l'Université
Chicoutimi, QC, G7H 2B1, Canada

Additional Information

Workshop URL

W02 — AI, Ethics and Society

The focus of this workshop is on the ethical and societal implications of building AI systems. It follows on from successful full day workshop held at AAAI-15. There is an increasing appetite within and outside AI to hold such discussions.

The workshop will consist of invited talks and tutorials, submitted papers, and one or more panel discussions.

Topics

Topics include but are not limited to the following:

• the future of AI
• AI as a threat to or saviour for humanity
• mechanisms to ensure moral behaviors in AI systems
• safeguards necessary within AI research
• autonomous agents in the military
• autonomous agents in commerce and other domains
• the impact of AI on work and other aspects of our lives

Persons wishing to present should submit a paper in the AAAI workshop style to the workshop submission website. There are no restrictions on the number of pages.

Workshop Chair

Toby Walsh NICTA
University of New South Wales
toby.walsh@nicta.com.au

Organizing Committee

Blai Bonet, Sven Koenig, Benjamin Kuipers, Illah Nourbakhsh, Stuart Russell, Moshe Vardi, Toby Walsh

Additional Information

Workshop URL

W03 — Artificial Intelligence for Cyber Security

The workshop will focus on research and applications of artificial intelligence to cyber security, including machine learning, game theory, natural language processing, knowledge representation, and automated and assistive reasoning. The workshop will emphasize cyber systems and research on techniques to enable resilience in cyber security systems augmented by human-machine interactions.

The human in the loop has been recognized as a common point of weakness in the defense of cyber systems by the cyber security community. One promising approach to mitigate this problem is to build models of human behavior and enable formal reasoning about how human beings interact with cyber systems. Game theory, in particular, has been used to model human behavior and leveraging game-theoretic models of defense for physical security, to suggest incentives and punishments that can be used to induce more secure behavior. Another promising approach is to design automated tools that can perform the task of the human in the loop. This approach mitigates security issues arising from sub-optimal security decision taken by humans. Further, automated tools can be formally specified more precisely, and with stronger guarantees, than human behavior; this enables stating and proving strong formal guarantees about security of the overall cyber-system.

Both of the above techniques for understanding how humans and systems interact have been extensively studied in AI, but not in the context of cyber security. Addressing these challenges requires collaboration between several different research and development communities including the artificial intelligence, cyber-security, game theory, machine learning and formal reasoning communities.

Topics

• Topics of interest include, but are not limited to:
• Machine learning approaches to make cyber systems secure
  Natural language processing techniques
  Anomaly/threat detection techniques
  Human behavioral modeling
• Formal reasoning, with focus on human element, in cyber systems
• Game theoretic reasoning in cyber security
• Economics of cyber security
• Multiagent interaction in cyber systems
• Modeling and simulation of cyber systems and system components
• Decision making under uncertainty in cyber systems
• Automated security aids for system administrators
• Quantitative human behavior models with application to cyber security

Format

Invited speakers, presentations, panel and group discussions

Attendance

We expect 20+ people to attend. Criteria for participants invitation: Papers addressing the intersection of AI and cybersecurity focusing on machine learning, game theory, assistive reasoning, modeling and simulation, predictive analytics, human-machine interactions will receive strong consideration in the invitation.

Submissions

Two formats are solicited: (1) Full-length papers (up to 8 pages in AAAI format) and (2) Challenge or position papers (2 pages in AAAI format) including introduction, novel contributions, results and future work Submissions are not anonymized. Please submit PDF via the workshop submission site by October 23, 2015.

Workshop Cochairs

David R. Martinez (MIT Lincoln Laboratory, MA, USA, dmartinez@ll.mit.edu), William W. Streilein (MIT Lincoln Laboratory, MA, USA, wws@ll.mit.edu), Kevin M. Carter (MIT Lincoln Laboratory, MA, USA, kevin.carter@ll.mit.edu), Arunesh Sinha (University of Southern California, CA, USA, aruneshsinha@gmail.com)

Program Committee

George Cybenko (Dartmouth College), Christos Dimitrakakis (Chalmers University of Technology, Sweden), Christopher Kiekintveld (University of Texas at El Paso), Robert Laddaga (DARPA/Information Innovation Office), Richard Lippmann (MIT Lincoln Laboratory), Mingyan Liu (University of Michigan), Daniel Lowd (University of Oregon), Christopher Miller (Smart Information Flow Technologies (SIFT)), Katerina Mitrokotsa (Chalmers University of Technology, Sweden), Ranjeev Mittu (Naval Research Laboratory), Howard Shrobe (MIT/CSAIL)

Administrative Contact

Cynthia Devlin-Brooks
MIT Lincoln Laboratory
244 Wood Street, Lexington, MA 02420
Voice: 781-981-7501
Fax: 781-981-4086
Email: Cynthia.Devlin-Brooks@ll.mit.edu

Additional Information

Workshop URL

W04 — Artificial Intelligence for Smart Grids and Smart Buildings

The proliferation of intelligent devices and the availability of electric monitoring facilities, broadband communication networks, computational intelligence, and customer-driven electricity storage and generation capabilities, have posed the foundations for the next generation power grids and buildings: smart grids and smart buildings.

AI plays a key role in smart grids and in smart buildings; the infrastructure provides information to support automated decision making on how to autonomously adapt production and consumption of energy, optimize costs, waste, and environmental impact, and ensure safe, secure, and efficient operation. The goal of this workshop is to bring together researchers from diverse areas of AI to explore both established and novel applications of AI techniques to address the design, implementation, and deployment of smart grids and smart buildings.

Topics

• Multiagent systems in smart grids and smart buildings
• Optimization methods for smart grids and smart buildings
• Machine learning mechanisms for smart grids and smart buildings
• Knowledge-based methods in design of smart grids and smart buildings
• Coordination of intelligent agents in smart grids and smart buildings
• Human-computer interactions within smart grids and smart buildings
• Negotiation and trading strategies in energy markets
• Simulations of energy markets, smart grids, and smart buildings

Format

The workshop will include three components: an invited keynote speaker; a collection of presentations selected from peer-reviewed submissions; and a closing panel to discuss future directions of research in this field.

Attendance

Approximately 50 people. Researchers interested in smart grids and smart buildings are invited to attend.

Submissions

Participants should submit a paper (maximum 6 pages + 1 page of references), describing their work on one or more of the topics relevant to the workshop, using the AAAI style files. Accepted papers will be presented during the workshop and will be published as AAAI technical reports, which will be made freely available in AAAI's digital library.

All submissions are conducted via EasyChair. Submissions should include the name(s), affiliations, and email addresses of all authors.

Organizing Committee

Enrico Pontelli (Chair) (New Mexico State University, epontell@cs.nmsu.edu); Alex Rogers (University of Southampton, acr@ecs.soton.ac.uk), Sylvie Thiebaux (Australian National University and NICTA, sylvie.thiebaux@nicta.com.au), Son Cao Tran (New Mexico State University, tson@cs.nmsu.edu)

Contact

aisgsb2016@easychair.org

Additional Information

Workshop URL

W05 — Beyond NP

A new computational paradigm has emerged in computer science over the past few decades, which is exemplified by the use of SAT solvers to tackle problems in the complexity class NP. According to this paradigm, a significant research and engineering investment is made towards developing highly efficient solvers for a prototypical problem (e.g., SAT), that is representative of a broader class of problems (e.g., NP). The cost of this investment is then amortized as these solvers are applied to a broader class of problems via reductions (in contrast to developing dedicated algorithms for each encountered problem). SAT solvers, for example, are now routinely used to solve problems in many domains, including diagnosis, planning, software and hardware verification.

The goal of this workshop is to help unify and promote research areas that advance this emerging computational paradigm, focusing on solvers that reach beyond NP. This includes, but is not limited to:

• Model counters, also known as #SAT solvers, which are now established as the prototypical solvers for the complexity class #P.
• Knowledge compilers, which reach to other problems in the polynomial and counting hierarchies.
• QBF solvers, which are now established as the prototypical solvers for the complexity class PSPACE.
• Solvers for function problems, including optimization and subset minimal problems, e.g. MaxSAT, MUS and MCS, that reach different levels of the function polynomial hierarchy.

These solvers are increasingly used to effectively tackle a broad class of problems (e.g., probabilistic graphical models, programming and databases; online configuration systems; verification, debugging, and testing).

Topics

Topics of interest to the workshop include algorithms; descriptions of implementations and/or evaluations of beyond NP solvers; their applications (including encodings); the complexity classes they reach; and their connections to one another. More broadly, submissions are solicited from three types of community members: those who develop solvers, those who use them to solve concrete problems, and those who are interested in the computational complexity of solvers and related problems. Submissions that can help disseminate “best practices” among the relevant research areas are also encouraged (e.g., competitions, benchmarks, and the development of open-source solvers).

Format

The format of the workshop will include presentations, posters and potentially panels.

Submissions

Submissions should be formatted using the AAAI conference style and not exceed 6 pages (shorter submissions are welcome). Submissions should be made through EasyChair and are expected to explicate relevance to one of the Beyond NP themes.

Organizing Committee

Adnan Darwiche (chair) (UCLA, darwiche@cs.ucla.edu), Joao Marques-Silva (University of Lisbon, jpms@tecnico.ulisboa.pt), Pierre Marquis (Universit´e d’Artois, marquis@cril.univ-artois.fr), Guy Van den Broeck (UCLA, guyvdb@cs.ucla.edu)

Program Committee

Fahiem Bacchus (University of Toronto, Canada), Supratik Chakraborty (IIT Mumbai, India), Stefano Ermon (Stanford University, USA), Mikolas Janota (Microsoft Research, Cambridge, UK), Matti Järvisalo (University of Helsinki, Finland), Marijn Heule (University of Texas, Austin, USA), Rupak Majumdar (Max-Planck Institute, Germany), Nina Narodytska (Samsung Research America, USA), Bart Selman (Cornell University, USA), Laurent Simon (University of Bordeaux, France), Dan Suciu (University of Washington, USA), Toby Walsh (NICTA, Australia), Stefan Szeider (Technical University of Vienna, Austria)

Additional Information

Workshop URL

W06 — Computer Poker and Imperfect Information Games

Recent years brought substantial progress in research on imperfect information games. There is an active community of researchers focusing on computer poker, which recently computed near optimal strategy for the smallest poker variant commonly played by people and achieved human level performance in more complex variants of this game. Game theoretic models with all sorts of uncertainty and imperfect information have been applied in security domains ranging from protecting critical infrastructure through green security (e.g., protecting wildlife and fisheries) to cyber security. Computer agents able to play a previously unknown imperfect information games only based on a formal description of its dynamics have been developed. 

In this AAAI-16 workshop, we aim to create a forum where researchers studying theoretical and practical aspects of imperfect information games can meet, present their recent results and discuss their new ideas. Moreover, we want to facilitate interaction between distinct communities studying various aspect and focusing on various domains in imperfect information games.

Topics

All topics related to theoretical or practical aspects of imperfect information games are of interest at the workshop. This includes for example descriptions of complete agents or novel components of agents playing specific imperfect information games, such as Poker or Bridge, imperfect information games modelling real world problems, or general game playing agents for imperfect information games. We welcome submissions analyzing formal representations of imperfect information games and their consequences on speed or optimality of game playing. We are also interested in opponent modelling techniques and human behavioural aspects specific for imperfect information games.

Format

The workshop will last a full day and will consist of both oral and poster presentations, as well as presentation of results and discussion about the AAAI Annual Computer Poker Competition. Anyone is welcome to attend the workshop; in the event of space constraints, priority will be given to people who submit papers or posters, or who participate in the Computer Poker Competition.

Submissions

Each submission will be in the form of an up to 8-page paper, using the main AAAI conference format. We leave to the authors if they want to anonymize their submissions or not. Papers should be submitted via Easy Chair.

Oral presentations and poster session participants will be selected from the submissions. The accepted papers will be published as a AAAI technical report.

Workshop Cochairs

Viliam Lisy (University of Alberta, lisy@ualberta.ca), Michael Thielscher (University of NewSouth Wales, mit@cse.unsw.edu.au), Thanh Nguyen (University of Southern California, thanhhng@usc.edu)

Additional Information

Workshop URL

W07 — Declarative Learning Based Programming

The main goal of the Declarative Learning Based Programming workshop is to investigate the issues that arise when designing and using programming languages that support learning from data and knowledge.

The Declarative Learning Based Programming workshop aims at facilitating and simplifying the design and development of intelligent real world applications that use machine learning and reasoning by addressing the following commonly observed challenges: Interaction with messy, naturally occurring data; Specifying the requirements of the application at a high abstraction level; Dealing with uncertainty in data and knowledge in various layers of the application program; Using representations that support flexible relational feature engineering; Using representations that support flexible reasoning and structure learning; Integrating a range of learning and inference algorithms; and finally addressing the above mentioned issues in one unified programming environment.

Conventional programming languages offer no help to application programmers that attempt to design and develop applications that make use of real world data, and reason about it in a way that involves learning interdependent concepts from data, incorporating existing models, and reasoning about existing and trained models and their parametrization. Over the last few years the research community has tried to address these problems from multiple perspectives, most notably various approaches based on probabilistic programming, logical programming and the integrated paradigms. The goal of this workshop is to present and discuss the current related research and the way various challenges have been addressed. We aim at motivating the need for further research toward a unified framework in this area based on the key existing paradigms: Probabilistic programing (PP), logic programming (LP), probabilistic logical programming (PLP), First-order query languages and database management systems (DBMS) and deductive databases (DDB), statistical relational learning and related languages (SRL), and connect these to the ideas of learning based programming.

We aim to discuss and investigate the required type of languages and representations that facilitate modeling probabilistic or non-probabilistic complex learning models, and provide the ability to combine, chain and perform flexible inference with existing models and by exploiting first-order background knowledge.

Topics

• Data modeling (Relational data modeling or Ggraph based)
• First-order Knowledge Representation
• Relational feature engineering
• Design and representation of complex Learning and inference models
• Probabilistic programming
• Probabilistic logical learning and reasoning
• Declarative languages
• Automation of hyper-parameter tuning
• Applications in natural language processing, computer vision and bioinformatics

Submissions

We encourage contributions in any of these areas with either a technical paper (AAAI style, 6 pageswithout references), a position statement (AAAI style, 2 pages maximum) or an abstract of a published work. For more details about the workshop see the workshop website. The submissions should be made via Easychair.

Organizing Committee

Parisa Kordjamshidi, Dan Roth, Avi Pfeffer, Guy Van den Broeck, Sameer Singh, Vivek Srikumar, Rodrigo de Salvo Braz

Additional Information

Workshop URL

W08 — Expanding the Boundaries of Health Informatics Using AI

The 20th century laid a foundation of evidence-based medicine that relied on populations and large groups of patients to derive generalized results and observations that were applied to (mostly passive) patients. Yet, the 21st century is shaping up as a time where the patient and personalized health data is the driver of health care innovation and delivery. The availability of this vast amount of personalized data allows for care tailored to a specific patient, an approach coined personalized medicine. Moreover, the availability of this data allows for the constant monitoring and discovery of deviations from patient-specific averages (possibly different from population-based averages). These deviations may signal developing problems and their early detection allows for more effective treatment leading to proactive medicine. Finally, patients are no longer passive recipients of (personalized) treatments and therapies, but they actively participate as a decision maker in their development, customization and application. This shift has lead to the emergence of participatory medicine.

To tackle issues across this spectrum of medicine, information technology will need to evolve to improve communication, collaboration, and teamwork between patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties. All of these changes require novel solutions and the AI community is well positioned to provide both theoretical- and application-based methods and frameworks. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation in order to provide high quality and efficient personalized care, and (5) connect patients with information beyond those available within their care setting. The extraction, representation, and sharing of health data, patient preference elicitation, personalization of “generic” therapy plans, adaptation to care environments and available health expertise, and making medical information accessible to patients are some of the relevant problems in need of AI-based solutions.

The Expanding the Boundaries of Health Informatics Using AI workshop will build on the very successful AAAI-13 Workshop on Expanding the Boundaries of Health Informatics using AI held at AAAI-13 in Seattle WA and the AAAI 2014 Fall Symposium in Arlington VA.

Submissions

Workshop participants are invited to submit either a full-length technical paper or a short position or demonstration paper. Full-length papers must be no longer than eight (8) pages, including references and figures. Short submissions can be up to four (4) pages in length and describe speculative work and work in progress on a topic of the workshop or a demonstration/tool.

Organizing Committee

Martin Michalowski, Chair (Adventium Labs, martin.michalowski@adventiumlabs.com, Jay M. Tenenbaum, Cochair (Cancer Commons), Szymon Wilk, Cochair (Poznan University of Technology)

Additional Information

Workshop URL

W09 — Incentives and Trust in Electronic Communities

Trust and incentive have bidirectional relationships. As trustworthiness measures are used as part of incentive mechanisms to promote honesty in electronic communities, incentive mechanisms motivate participants to contribute their truthful opinions that are useful for trust modeling. Hence, trust and reputation systems should not only provide a means to detect and prevent malicious activities but also design a mechanism to discourage dishonesty attitudes amongst participants.

The evidential success of combining these two concepts inspires and encourages researchers in the trust community to enhance the efficacy and performance of trust modeling approaches by adopting various incentive mechanisms.

The main objective of this workshop is to bring together researchers and practitioners of both fields, to foster an exchange of information and ideas, and to facilitate a discussion of current and emerging topics relevant to building effective trust, reputation and incentive mechanisms for electronic communities.

Topics

Topics of interest include, but are not limited to:

• Social, cognitive trust, reputation
• Computational trust, reputation
• Incentive mechanisms
• Cross-cultural approaches
• Components and dimensions of sociotechnical trust
• Game theoretic approaches to trust and reputation
• Game theory and trusting behaviours
• Risk management and trust-based decision making
• Trust management dynamics
• Trust, regret, and forgiveness
• Economic drivers for trustworthy systems
• Trust and economic models
• Trust metrics assessment and threat analysis
• Context-aware trust assessments
• Trust-aware recommender systems
• Evolution of trust
• Trust-based incentive mechanisms
• Robustness of trust and reputation systems
• Trust metrics assessment and threat analysis
• Robustness of incentive mechanisms
• Deception and fraud, and its detection and prevention
• Attacks on, and defences for, trust, reputation and incentive mechanisms
• Testbeds and framework of trust
• User interfaces to incentive mechanisms
• Real-world applications for virtual communities (e.g. e-commerce, social network, e-health, e-learning, blog, online tutoring systems)

Submissions

Papers must be formatted according to the AAAI 2016 style guide. We solicit short and long papers as well as research demos. Long papers (6 pages) present original research work; short papers (4 pages) report on work in progress or describe demo systems. All the selected papers will be published in an AAAI technical report volume.

Submissions will be reviewed for relevance, originality, significance, validity and clarity. All articles selected for publication will be reviewed by at least two reviewers with expertise in the area.

Workshop Cochairs

Jie Zhang (Nanyang Technological University, Singapore), Zeinab Noorian (University of Ryerson, Canada), Stephen Marsh (University of Ontario Institute of Technology, Canada)

Advisory Committee

Robin Cohen (University of Waterloo, Canada), Sandip Sen (University of Tulsa, USA), Yuko Murayama (Iwate Prefectural University, Japan), Karl Aberer (EPFL, Switzerland), Rino Falcone (ISTC-CNR, Italy), Christian Jensen (Technical University of Denmark, Denmark)

Contact

zeinab.noorian@gmail.com

Additional Information

Workshop URL

Cancelled W10 — Integration of Learning and Diagnosis

There is promising opportunity in overlapping model-based reasoning and data-driven learning approaches. In model-based reasoning, a model describes the behavior of an underlying system. In data-driven machine learning, a model describes interdependence of properties of some entity that may or may not come from an underlying system.

The 2016 AAAI Workshop on Integration of Learning and Diagnosis focuses on an interdisciplinary research that integrates machine learning and model-based diagnosis. This is a subarea of the larger overlap of model-based reasoning and data-driven machine learning, particularly in the context of diagnosis. Research in diagnosis focuses on identifying the root causes for encountered issues.

The workshop aims to encourage interaction and collaboration across researchers and practitioners with diverse backgrounds including artificial intelligence (AI), machine learning, model-based diagnosis, control theory, cyber security and software engineering. The goal is to leverage a diverse set of expertise to combine model-based diagnosis and machine learning to not only increase accuracy but also extend capabilities by solving problems formerly not conducive to analytic approaches.

The workshop will provide a forum to present current research and experience reports, exchange and discuss emerging ideas and promising directions, debate current issues and envisioned future challenges, as well as share data resources to help advance this research direction.

Topics

We are looking forward to submissions on topics that combine and/or contrast machine learning and diagnosis problems and challenges, including papers focusing on the following issues:

• Automatic learning of behavioral models by characterizing knowledge from data.
• Applying machine learning to construct behavioral models.
• Leveraging machine learning results to tune the parameters of the already known (first principles) physics model.
• Devising diagnosis techniques to improve and explain machine learning results.
• Identifying challenges of applying existing machine learning techniques for diagnosis problems.
• Exploring directions for a hybrid model-based and data-driven approach for other application domains such as cyber security and condition-based maintenance.

Format

The workshop will run for a full day and will include a keynote talk, paper presentations, a panel and a poster session.

Attendance

The intended audience includes researchers and practitioners interested in artificial intelligence (AI), machine learning, model-based diagnosis, control theory, cyber security and software engineering.

Submissions

The workshop will accept two types of papers. Regular research papers must not be longer than eight pages in the double column A4 format provided at the workshop's website. The second type of accepted papers are short papers (four pages max) on preliminary works presenting position ideas for new problems.

Authors are required to submit their papers electronically to the EasyChair URL listed on the workshop website. All submissions will be peer-reviewed, and accepted papers will be scheduled for either an oral or a poster presentation.

Workshop Chair

Hoda Eldardiry
Palo Alto Research Center
Address: 3333 Coyote Hill Road, Palo Alto, California, 94304
Phone: 650-812-4790
Fax: 650-812-4237
Email: Hoda.eldardiry@parc.com

Organizing Committee

Rui Abreu (Palo Alto Research Center, rui@parc.com), Johan de Kleer (Palo Alto Research Center, dekleer@parc.com), Daniel Bobrow (Palo Alto Research Center, bobrow@parc.com)

Additional Information

Workshop URL

W11 — Knowledge Extraction from Text

Text understanding is an old, but as yet unsolved, AI problem consisting of a number of nontrivial steps. The critical step in solving the problem is knowledge acquisition from text, i.e. a transition from a non-formalized text into a formalized language that drives computer actions. Many of required steps in the text understanding pipeline, including linguistic processing, reasoning, text generation, search, question answering etc., are already solved to a degree that allows composition of text understanding services. We know that knowledge acquisition, the key bottleneck, can be done by humans, but automating of the process is still out of reach in its full breadth.

In recent years interest in text understanding and knowledge acquisition from text has been growing. Many AI research groups are addressing relevant aspects of computational linguistics, machine learning, probabilistic and logical reasoning, and the semantic web. The goal of the workshop is to bring together experts from the diverse fields working towards text understanding.

The workshop is a continuation of the Knowledge Extraction from Text workshop from NIPS 2013 and the 2nd Workshop on Knowledge Extraction from Text workshop from WWW2015.

Topics

Topics of interest include, but are not limited to the following:

• cross-lingual, multilingual and monolingual alignment between knowledge bases and text
• joint inference between text interpretation and knowledge bases
• textual natural language processing and natural language understanding
• machine reading, reading the web, and learning by reading
• macro-reading, micro-reading and information retrieval
• supervised, unsupervised, semi-supervised and distantly-supervised learning
• crowdsourcing, human computation and conversational learning
• knowledge base construction and population from text
• text-based question-answering

Submissions

We invite submissions via EasyChair on all aspects of text understanding, including approaches related to areas of computational linguistics, machine learning, knowledge representation, probabilistic and logical reasoning, and the semantic web. More details on the submission format, please see the Workshop web site.

Organizing Committee

Marko Grobelnik (Jozef Stefan Institute, Slovenia), Estevam Hruschka (Federal University of Sao Carlos, Brazil), Michael Witbrock (Cycorp Inc, Austin, Texas), Blaz Fortuna (iMinds – Ghent University, Belgium)

Additional Information

Workshop URL

W12 — Multiagent Interaction without Prior Coordination

This workshop focuses on models and algorithms for multiagent interaction without prior coordination (MIPC). Interaction between agents is the defining attribute of multiagent systems, encompassing problems of planning in a decentralized setting, learning other agent models, composing teams with high task performance, and selected resource-bounded communication and coordination. There is significant variety in methodologies used to solve such problems, including symbolic reasoning about negotiation and argumentation, distributed optimization methods, machine learning methods such as multiagent reinforcement learning, etc. The majority of these well-studied methods depends on some form of prior coordination. Often, the coordination is at the level of problem definition. For example, learning algorithms may assume that all agents share a common learning method or prior beliefs, distributed optimization methods may assume specific structural constraints regarding the partition of state space or cost/rewards, and symbolic methods often make strong assumptions regarding norms and protocols. In realistic problems, these assumptions are easily violated – calling for new models and algorithms that specifically address the case of ad hoc interactions. Similar issues are also becoming increasingly more pertinent in human-machine interactions, where there is a need for intelligent adaptive behaviour and assumptions regarding prior knowledge and communication are problematic.

Effective multiagent interaction without prior coordination is most likely to be achieved as we bring together work from many different areas, including work on intelligent agents, machine learning, game theory, and operations research. For instance, game theorists have considered what happens to equilibria when common knowledge assumptions must be violated, agent designers are faced with mixed teams of humans and agents in open environments and developing variations on planning methods in response to this, etc. The goal of this workshop is to bring together these diverse viewpoints in an attempt to consolidate the common ground and identify new lines of attack.

This workshop is the third edition of the Multiagent Interaction without Prior Coordination workshop series, previously held at AAAI-15 in Austin, Texas, USA, and AAAI-14 in Quebec City, Canada.

Topics

The workshop will discuss research related to multiagent interaction without prior coordination, as outlined in the workshop description above. A nonexclusive list of relevant topics includes the following:

• Agent coordination and cooperation without prior coordination
• Learning and adaptation in multiagent systems without prior coordination
• Team formation and information sharing in ad hoc teamwork settings
• Human-machine interaction without prior coordination
• Teammate/opponent modelling and plan recognition without prior coordination
• Game theory/incomplete information applied to ad hoc agent coordination
• Empirical and theoretical investigations of issues arising from prior assumptions
• Ad hoc coordination in the presence of adversaries

Format

The one-day workshop will include keynote talks from invited speakers, sessions of oral workshop paper presentations, and an “open problems and discussion” session.

Submissions

The workshop follows the formatting guidelines for standard paper submissions to the AAAI-16 main track. Workshop papers can be submitted via EasyChair and will be selected based on a single-blind peer review process.

Program Chairs

Stefano Albrecht (University of Edinburgh, s.v.albrecht@sms.ed.ac.uk); Katie Genter (University of Texas at Austin, katie@cs.utexas.edu); Somchaya Liemhetcharat (A*STAR Singapore, liemhet-s@i2r.a-star.edu.sg)

Advisory Committee

Subramanian Ramamoorthy (University of Edinburgh, s.ramamoorthy@ed.ac.uk); Peter Stone (University of Texas at Austin, pstone@cs.utexas.edu); Manuela Veloso (Carnegie Mellon University, mmv@cs.cmu.edu)

Contact

mipc2016@easychair.org

Additional Information

Workshop URL

W13 — Planning for Hybrid Systems

The purpose of the workshop is to explore and promote new approaches to planning with hybrid models. Hybrid systems are systems with both continuous control variables and discrete logical modes. Many interesting real problems are indeed hybrid systems, including oil refinery management, mission planning for autonomous vehicles, supply management and disaster recovery, and applications in control of smart cities.

Planning in these domains requires rich models to capture the interaction between discrete and continuous change, and methods for reasoning with temporal, spatial and continuous constraints.

This is intended as a multi-disciplinary workshop, and aims to put together researchers from planning, robotics, machine learning, hybrid system control and verification, model-based reasoning.

Topics

• Planning with mixed discrete-continuous dynamics
• Planning with hybrid stochastic dynamics
• Hybrid systems applications
• Analysis, verification and control of hybrid systems
• Planning/control interaction
• Novel benchmark problems involving hybrid dynamics
• Plan monitoring and execution
• Plan robustness
• Plan validation
• Planning and model checking

Format

The workshop will include invited talks, presentations of accepted contributions, and discussion. To accommodate this program, the expected duration of the workshop is 1 day.

Submissions

Papers should be accessible to researchers from AI planning, robotics, hybrid system control and verification. Two formats are solicited: full-length papers (up to 8 pages in AAAI format) or challenge or position papers (2 pages in AAAI format). All papers will be peer reviewed. Papers should be submitted in PDF via EasyChair.

Organizing Committee

Daniele Magazzeni (King's College London), Scott Sanner (Oregon State University), Sylvie Thiebaux (ANU and NICTA)

Additional Information

Workshop URL

W14 — Scholarly Big Data: AI Perspectives, Challenges, and Ideas

Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, and course materials. For example, PubMed has over 20 million documents, 10 million unique names and 70 million name mentions. Google Scholar has many millions more, it is believed. Understanding how at scale research topics emerge, evolve, or disappear, what is a good measure of quality of published works, what are the most promising areas of research, how authors connect and influence each other, who are the experts in a field, and who funds a particular research topic are some of the major foci of the rapidly emerging field of Scholarly Big Data.

Digital libraries, repositories, databases, Wikipedia, funding agencies and the Web have become a medium for answering such questions. For example, citation analysis is used to mine large publication graphs in order to extract patterns in the data (e.g., citations per article) that can help measure the quality of a journal. Scientometrics is used to mine graphs that link together multiple types of entities: authors, publications, conference venues, journals, institutions, etc., in order to assess the quality of science and answer complex questions such as those listed above. The recent developments in Artificial Intelligence technologies make it possible to transform the way we analyze research publications, funded proposals, patents, etc., on a Web-wide scale.

Topics

The workshop aims at bringing together researchers with diverse interdisciplinary backgrounds interested in mining, managing and searching scholarly big data using new AI technologies or analyzing their transferability from one domain to another. The topics of interest include, but are not limited to: (a) New AI approaches to measuring the impact of research funding and publications as well as the impact of researchers in a particular field of study: Identifying influential authors, experts, and collaborators within or across disciplines; Modeling the referencing behavior across disciplines; Automatic citation recommendation; (b) Mining large digital libraries of scientific publications and linking to other databases such as funded proposals and patents: Identifying research trends and topics; Extracting relevant information from research articles, including an article's metadata and keyphrase extraction; Scaling up machine learning algorithms to large research and related datasets; Classification and clustering of scientific trends, publications, funded proposal, patents, etc; Large scale linking of various entities, e.g., articles with articles by similarity, articles with their corresponding presentation slides, articles with the corresponding funded proposals; (c) Presenting open-access, novel datasets (e.g., based on Wikipedia, DBpedia, United States Census Bureau data, Patent data, etc.) that can be linked to entities, and can help researcher develop novel technologies for analyzing scientific publications; (d) Effectively indexing and searching large scale academic documents and other resources.

Format

The workshop will have a series of presenters (10 to 12 paper presentations) and two one-hour keynote talks.

Submissions

Submissions related to the above topics are invited. Papers must not exceed six pages, must be written in English, and must be formatted according to the AAAI style files. We encourage contributions describing either new problems in scholarly big data or work on established problems using novel approaches. Submissions will made through EasyChair.

Organizing Committee

Cornelia Caragea (University of North Texas, USA), C. Lee Giles (Pennsylvania State University, USA), Alex D. Wade (Microsoft Research, USA), Doina Caragea (Kansas State University, USA), Vu Ha (Allen Institute for Artificial Intelligence, USA), Irwin King (The Chinese University of Hong Kong, Shatin, NT, Hong Kong), Jie Tang (Tsinghua University, Beijing, China).

Additional Information

Workshop URL

W15 — Symbiotic Cognitive Systems

In his 1960 article on Man-Machine Symbiosis, Licklider predicted a time when “the main intellectual advances will be made by men and computers working together in intimate association”. While much of the emphasis within the AI community over the ensuing half century was placed upon tools for automation such as speech recognition or surpassing humans at challenging intellectual tasks such as chess or Jeopardy!, the last few years have witnessed a resurgent interest in symbiotic cognitive systems: collectives of humans and intelligent agents that collaborate to accomplish cognitive tasks better than either can alone.

The objective of this workshop is to bring together researchers working on aspects of symbiotic cognitive computing in various application domains to synthesize a new vision and research agenda for symbiotic cognitive systems and establish a community that will have a continued existence at future AI workshops and conferences.

Topics

The workshop will explore technologies, architectures, applications, or other aspects of symbiotic cognitive computing, including but not limited to the following:

• Cognitive assistants;
• Natural and/or multi-modal interaction technologies, e.g. dialog, gesture
• Technologies that support collaboration among humans and intelligent agents;
• Technologies for creating, representing or using shared context as a vehicle for communication and understanding among humans and intelligent agents;
• Planning for symbiotic tasks and/or interactions;
• Agents that learn models of the environment and humans or other agents situated within it
• Software or hardware architectures for symbiotic cognitive systems;
• Descriptions and/or evaluations of symbiotic computing systems applied to application domains
• Metrics and benchmarks for assessing the cognitive capabilities of human-agent collectives;
• Architectures for cognitive agents or cognitive systems;
• Theoretical topics pertinent to symbiotic cognitive systems; and
• Vision statements for the future of symbiotic cognitive systems.

Format

Full day of short invited talks, presentations based upon 4-page selected submissions, and moderated discussions among all participants.

Attendance

25-40 people, invited on basis of involvement in field and/or submitted papers.

Submissions

Papers up to 4 pages in length in .pdf format based on a current AAAI style file that can be found online. Submission via email, with subject line prefaced with [AAAI2016-SCS workshop submission].

Submit to: kephart@us.ibm.com (1 914 945-2540) and srpomerantz@sei.cmu.edu

Workshop Cochairs

Jeffrey O. Kephart (kephart@us.ibm.com), Stephanie Rosenthal (srpomerantz@sei.cmu.edu), Alex Rudnicky (alex.rudnicky@cs.cmu.edu), Manuela Veloso (mmv@cs.cmu.edu).

Additional Information

Workshop URL

W16 — World Wide Web and Population Health Intelligence

Public health authorities and researchers collect data from many sources, and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns. To provide proper alerts and timely response public health officials and researchers systematically gather news, and other reports about suspected disease outbreaks, bioterrorism, and other events of potential international public health concern, from a wide range of formal and informal sources.

Given the ever increasing role of the world wide web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Web applications along with text processing programs are increasingly being used to harness online data and information to discover meaningful patterns identifying emerging health threats. The advances in web science and technology for data management, integration, mining, classification, filtering and visualization has given rise to variety of applications representing real time data on epidemics.

Topics

The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in public and population health. The scope of the workshop includes, but is not limited to, the following areas:

• Geographical mapping and visual analytics for health data
• Social media analytics
• Epidemic intelligence
• Predictive modelling and decision support
• Knowledge representation, semantic web, and web services
• Biomedical ontologies, terminologies and standards
• Bayesian networks and reasoning under uncertainty
• Temporal and spatial representation and reasoning
• Case-based reasoning in healthcare
• Crowdsourcing, and collective intelligence
• Risk assessment, trust, ethics, privacy, and security
• Sentiment analysis and opinion mining
• Computational behavioral/cognitive modeling
• Health intervention design, modeling and evaluation
• Online health education and e-learning
• Mobile web interfaces and applications
• Applications in epidemiology and surveillance (e.g. bioterrorism, participatory surveillance, population screening)

Following the success of the 2014 and 2015 workshops, this workshop aims to bring together a wide range of computer scientists, researchers, students, industry professionals, national and international public health agencies, and NGOs interested in the theory and practice of computational models of web-based population health intelligence to highlight the latest achievements in epidemiological surveillance based on monitoring online communications and interactions on the world wide web. The workshop promotes open debate and exchange of opinions among participants.

Format

The workshop will be one full day consist of welcome session, keynote and invited talks, full/short paper presentations, demos, posters, and a panel discussion.

Attendance

Estimated number of attendance: 25-30

Submissions

We invite researchers and industrial practitioners to submit their original contributions following AAAI format through EasyChair. Three categories of contribution are sought: full-research papers up to 8 pages; short paper up to 4 pages; and posters and demos up to 2 pages.

Workshop Cochairs

Arash Shaban-Nejad, PhD.
McGill Clinical & Health Informatics,
McGill University
1140 Pine Avenue West,
Montreal, Quebec, H3A 1A3 CANADA,
(514) 934-1934 ext. 32986 (tel)
(514) 843-1551 (fax)
Email: arash.shaban-nejad@mail.mcgill.caa
arash.shaban-nejad@berkeley.edu
URL

David L. Buckeridge, MD, PhD.
McGill Clinical and Health Informatics,
McGill University
1140 Pine Avenue West,
Montreal, Quebec, H3A 1A3 CANADA,
(514) 398-8355 (tel)
(514) 843-1551 (fax)
Email: david.buckeridge@mcgill.ca
URL

Byron C. Wallace, PhD
School of Information & Department of Computer Science,
University of Texas at Austin,
1616 Guadalupe Suite #5.202
Austin, Texas 78701-1213
(512) 471-3971 (Fax)
Email: byron.wallace@utexas.edu
URL

John S. Brownstein, PhD.
Boston Children's Hospital,
Harvard University,
Autumn St, Room 451,
Boston, MA 02215 USA,
(617) 355-6998 (tel)
(617) 730-0921 (fax)
Email: john_brownstein@harvard.edu
URL

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