Sponsored Workshops (AAAI-18)

Submissions should be made via the EasyChair submission site and must follow the formatting guidelines for AAAI-2018 using the AAAI-18 Author Kit. All submissions must be anonymous and conform to AAAI standards for double-blind review.

Cochairs

Niyati Chhaya, primary contact (Adobe Research, nchhaya@adobe.com), Kokil Jaidka (University of Pennsylvania, jaidka@sas.upenn.edu), Lyle Ungar (University of Pennsylvania, ungar@cis.upenn.edu), P. Anandan (p.anandan55@outlook.com)

Additional Information

W02 — Artificial Intelligence and Marketing Science

The revolution in the digital economy has rapidly changed the way companies manage, execute and measure the effectiveness of marketing strategies and the delivery of marketing products and services. The widespread growth in digital marketing tools and platforms have led to diverse sources of marketing, advertising and consumer behavioral data, often available in real time. This opens the door to the application of a wide range of AI techniques in areas traditionally considered parts of the marketing science (MS). For example, this includes the use of machine learning, deep learning, sequential decision making, bandits and sequential testing, recommendation systems, game theory, knowledge representation, market design and optimization, and so on for the purpose of marketing resource optimization, managerial decision making, competitive behavior modeling, deconstruction of consumer behavior, and campaign automation and optimization.

Research in this field has been largely carried in separate communities until now. Within the AI and machine learning community, the focus has been on developing new and more efficient computational models and techniques, geared towards specific tasks. Within the MS community, the focus has been on exploiting machine learning methods and scalable data methods for addressing important business problems that marketers face. Consequently, researchers publish in separate journals and conferences. It is our conviction that these two separate communities have a lot to benefit from each other’s work, problems and insights.

Topics

This workshop seeks to bring together researchers and practitioners from AI and from MS communities to share in ideas, challenges, opportunities and successes. It will aim to identify important research directions and to identify opportunities for synthesis and unification. In particular, we are calling for research contributions in the following areas:

Optimizing marketing decisions under resource constraint
Automated decision making with feedback
Optimizing spend across channels
Automation and optimization of marketing campaign
Optimization in the delivery or marketing messages
Understanding consumer psychology personalization and optimization of marketing assets and contents
Bandits and sequential testing for marketing strategies.
Customer journey modeling
Consumer life time value optimization and sequential decision making
Personalization and recommendation
Intent recognition and user modeling on the web and in marketplaces and e-commerce
Causal inference and measuring the effectiveness of marketing message
Automatic clustering and audience segmentation
Predictive analytics and forecasting of key performance metrics in commerce
Applications of deep learning in predictive analytics
Representation learning for marketing data
Knowledge representation for marketing science insights
Learning in games and mechanism design for marketing
Social games and marketing methods.

Due to the diversity of disciplines represented in this area, related contributions in other fields are also welcome.

Format

We solicit research contributions that report new research results or ongoing research. The workshop’s proceedings can be considered nonarchival, meaning contributors are free to publish their results later in archival journals or conferences. Research contributions should follow the AAAI formatting guideline and template and should be limited to a maximum of 6 pages in AAAI format excluding the references.

Submissions

Submissions should be sent to aims2018submit@gmail.com; by October 13. Acceptance notification will be sent out on November 9, 2017.

Workshop Cochairs

Hung Bui (Adobe Research), Pradeep Chintagunta (University of Chicago), S. Muthukrishnan (Rutgers University), Tuomas Sandholm (Carnegie Mellon University), Atanu Sinha (Adobe Research and Emeritus, University of Colorado), Georgios Theocharous (Adobe Research)

Additional Information

W03 — 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, and others)
Algorithms for intelligent proactive assistance
Context awareness
High-level activity and event recognition
Multiperson 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 paper 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, for poster presentations, a short paper or extended abstract up to 2 pages describing research relevant to the workshop.

Chairs and Cochairs

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

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

Kévin Bouchard, Ph.D
418 545-5011 (5063) | Kevin.Bouchard@uqac.ca
555, boul. de l’Université, Chicoutimi, QC, G7H 2B1 Canada

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

Additional Information

W04 — Artificial Intelligence for Cyber Security

This workshop will focus on the application of artificial intelligence to problems in cyber security. This year the workshop’s emphasis will be on Internet of Things (IoT) and mobile devices relative to cyber security. The workshop will address technologies and their applications, such as machine learning, game theory, natural language processing, knowledge representation, automated and assistive reasoning, and human machine interactions. The workshop will emphasize cyber systems and research on techniques to enable resilience in mobile systems involving human-machine interactions.

IoT and mobile devices provide powerful sensing and computing capabilities to users and systems. These same capabilities, however, offer new opportunities for adversarial compromise, resulting in the loss of data and control, and could lead to infection spread to other connected devices. Artificial intelligence capabilities have the potential to help protect these mobile platforms in several ways. Because IoT mobile devices, such as smart phones, are often used by one individual at a time, behavioral analysis techniques that model normal user usage patterns can be leveraged to recognize anomalous, or out-of-the-ordinary, behaviors that might be indicative of misuse. In addition, graph analysis of a device’s connectivity network, such as a user’s social network or links in a web page, can be leveraged to identify malicious sites that could seek to infect the devices. Finally, AI capabilities that collect, correlate, and analyze multiple data sources at once can be leveraged to confirm the provenance and veracity of data from suspect IoT devices.

Addressing the cyber security challenges of IoT and mobile devices requires collaboration between several different research and development communities including the artificial intelligence, cyber-security, game theory, machine learning, and formal reasoning communities.

The aforementioned applications of AI have the potential to impact cyber security in a positive way, bringing automated learning and game theory into the service of improved system resilience. Developing and applying these and other AI capabilities to cyber security problems requires collaboration between several different communities including the artificial intelligence, game theory, machine learning, and cyber-security communities, as well as the operational and commercial applications communities. This workshop is structured to encourage a lively exchange of ideas between researchers in these communities from the academic, public, and commercial sectors.

Topics

Machine learning approaches to make cyber systems secure and resilient
- Natural language processing techniques
- Anomaly/threat detection techniques
- Big data noise reduction 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/agent-based modeling 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
Operational and commercial applications of AI

Challenge Problem

For information on this year’s AICS challenge problem please see the Workshop URL. The Challenge Problem is sponsored by the CrowdStrike Foundation.

Format

The workshop will consist of invited speakers, presentations, and panel and group discussions

Submissions

One of two submissions is solicited: Full-length papers (up to 8 pages in AAAI format) or Challenge Problem papers (up to 8 pages in AAAI format).

Submissions are not anonymized. Please submit PDF via the workshop URL website by October 23, 2017. Accepted papers will be published in the workshop proceedings.

Cochairs

William W. Streilein (MIT Lincoln Laboratory, MA, USA, wws@ll.mit.edu), David R. Martinez (MIT Lincoln Laboratory, MA, USA, dmartinez@ll.mit.edu), Howard Shrobe (MIT/CSAIL, MA, USA, hes@csail.mit.edu), Arunesh Sinha (University of Michigan, MI, USA, aruneshsinha@gmail.com), Neal Wagner (MIT Lincoln Laboratory, MA, USA, Neal.wagner@ll.mit.edu), Cem Sahin (MIT Lincoln Laboratory, MA, USA, Cem.Sahin@ll.mit.edu)

Program Committee

George Cybenko (Dartmouth College), Christos Dimitrakakis (Chalmers University of Technology, Sweden), Robert Goldman (Smart Information Flow Technologies (SIFT)), Christopher Kiekintveld (University of Texas at El Paso), Robert Laddaga (Vanderbilt University), 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), Sven Krasser (Crowdstrike), Benjamin Rubinstein (University of Melbourne, Australia), Robert Templeman (Navy Surface Warfare Center, Crane Division)

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

Additional Information

W05 — Artificial Intelligence for Imperfect-Information Games

Recent years brought substantial progress in research on imperfect-information games. Superhuman performance has been achieved in large-scale variants of poker. Game theoretic models with all sorts of uncertainty have been applied in security domains ranging from protecting critical infrastructure through green security (for example, 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-18 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 modeling techniques and human behavioral 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 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 EasyChair. Oral presentations and poster session participants will be selected from the submissions.

Workshop Chairs

Noam Brown (Carnegie Mellon University, noamb@cs.cmu.edu, Marc Lanctot (Google DeepMind, lanctot@google.com), Haifeng Xu (University of Southern California, haifengx@usc.edu)

Additional Information

W07 — Declarative Learning Based Programming

The main goal of 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. Declarative learning based programming aims at new programming models and abstractions that facilitate the design and development of intelligent real world applications that use machine learning, deep learning and reasoning.

The challenges of such a paradigm include interaction with messy, naturally occurring data; specifying the requirements of the application at a high abstraction level; dealing with uncertainty in various layers of the application program; supporting flexible relational feature engineering and learning rich data representations; using representations that support flexible reasoning, structured and deep learning; supporting model chaining and composition; 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 and composing existing models, and reasoning about existing and trained models and their parameterization. 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 programming, logic programming, probabilistic logical programming, first-order query languages and database management systems and deductive databases, statistical relational learning and related languages, declarative deep learning frameworks and connect these to the ideas of declarative learning based programming. We aim to discuss and investigate the required type of languages and representations that facilitate modeling complex learning models, deep architectures, and provide the ability to combine, chain and perform flexible inference by exploiting domain knowledge.

Though the theme of this workshop remains generic, we aim at emphasizing on ideas and opinions regarding conceptual representations of deep learning architectures that connect various computational units to the semantics of declarative data and knowledge representations. We are interested in the abstractions that in contrast to the existing ones (for example, tensor flow), are away from the underlying computational units and are towards declarative domain representations while are expressive enough to exploit the deep configurations and computations.

Submissions

We encourage contributions with either a technical paper (AAAI style, 6 pages without references), a position statement (AAAI style, 2 pages maximum) or an abstract of a published work. Please make submissions via EasyChair. Detailed submission instructions can be found on the workshop website.

Organizing Committee

Parisa Kordjamshidi (Tulane University / Florida Institute for Human & Machine Cognition, pkordjam@tulan.edu), Dan Roth (University of Pennsylvania, danroth@seas.upenn.edu), Kristian Kersting (TU Darmstadt, kersting@cs.tu-darmstadt.de), Dan Goldwasser (Purdue University, dgoldwas@purdue.edu), Nikolaos Vasiloglou II (Ismion Inc, nvasil@gmail.com)

Additional Information

W08 — Engineering Dependable and Secure Machine Learning Systems

Modern society increasingly relies on machine learning (ML) solutions. Like other systems, ML systems must meet their requirements. Standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage or traditional software debugging become practically irrelevant for ML systems. This is due to the nondeterministic nature of ML systems, reuse of high quality implementations of ML algorithms, and lack of understanding of the semantics of learned models, for example, when deep learning methods are applied.

For example, self-driving car models may have been learned in a cold weather country. When such a car is deployed in a hot weather country, it will likely face dramatically different driving conditions that may render its models obsolete. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems.

Furthermore, broad deployment of ML software in networked systems inevitably exposes the ML software to attacks. While classical security vulnerabilities are relevant, ML techniques have additional weaknesses, some already known (for example, sensitivity to training data manipulation), and some yet to be discovered. Hence, there is a need for research as well as practical solutions to ML security problems.

With these in mind, this workshop solicits original contributions addressing problems and solutions related to dependability, quality assurance and security of ML systems. The workshop combines several disciplines, including ML, software engineering (with emphasis on quality), security, and algorithmic game theory. It further combines academia and industry in a quest for well-founded practical solutions.

Topics

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

Software engineering aspects of ML systems and quality implications
Testing and debugging of ML systems
Quality implication of ML algorithms on large-scale software systems
Case studies of successful and unsuccessful applications of ML techniques
Correctness of data abstraction, data trust
- ML techniques to meet security and quality
- Size of the training data, implied guaranties
- Application of classical statistics to ML systems quality
Sensitivity to data distribution diversity and distribution drift
The effect of labeling costs on solution quality (semi-supervised learning)
Reliable transfer learning
Vulnerability, sensitivity and attacks against ML
Adversarial ML and adversary based learning models
Strategy-proof ML algorithms

Submissions

We solicit original papers in two formats – full (8 pages) and short (4 pages, work in progress), in AAAI format. Submission is via EasyChair. All authors of accepted papers will be invited to participate. The workshop will include paper presentation sessions. Full papers are allocated 20m presentation and 10m discussion. Short papers 10m presentation + 5m discussion. The last session will be a panel discussion.

Chairs

Dr. Eitan Farchi
DE, Software Testing Analysis and Reviews,
IBM Research, Haifa, Israel
Tel: +972-4-8296154 | Fax: +972-4-8296114 | Mobile: +972-54-4373352
Email: farchi@il.ibm.com

Prof. Onn Shehory
Information Systems, Graduate School of Business Administration,
Bar Ilan University, Israel
Tel: +972-3-5318362 | Fax: +972-6325239 | Email: onn.shehory@biu.ac.il

Dr. Anna Zamansky
Information Systems Department, University of Haifa, Israel
Tel: +972-545402870 | Email: annazam@is.haifa.ac.il

Prof. Ilan Shimshoni
Information Systems Department, University of Haifa, Israel
Tel: +972-4-8288510 | Email: ishimshoni@is.haifa.ac.il

Additional Information

W09 — 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 nontraditional 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 a variety of applications representing real time data on epidemics.

Moreover, to tackle and overcome several issues in personalized healthcare, 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. 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 will improve health outcomes. 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.

This two-day workshop will address various aspects of using AI for improving population and personalized healthcare and is structured in two tracks focusing on population (W3PHI) and personalized health (HIAI). This workshop aims to bring together a wide range of computer scientists, clinical and health informaticians, researchers, students, industry professionals, national and international health and public health agencies, and NGOs interested in the theory and practice of computational models of population health intelligence and personalized healthcare. The workshop promotes open debate and exchange of opinions among participants.

Topics

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

Knowledge representation and extraction
Integrated health information systems
Patient education
Patient-focused workflows
Shared decision making
Geographical mapping and visual analytics for health data
Social media analytics
Epidemic intelligence
Predictive modeling and decision support
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 (for example, bioterrorism, participatory surveillance, syndromic surveillance, population screening)

Format

The workshop will be two full days consisting of a welcome session, keynote and invited talks, full/short paper presentations, demos, posters, and one or two panel discussions.

Submissions

EasyChair Submission Site

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

Organizing Committee

Martin Michalowski, Cochair, (University of Minnesota – Twin Cities, martinm@umn.edu); Arash Shaban-Nejad, Cochair, (The University of Tennessee Health Science Center – Oak-Ridge National Lab Center for Biomedical Informatics, ashabann@uthsc.edu); Szymon Wilk (Poznan University of Technology, szymon.wilk@cs.put.poznan.pl); David L. Buckeridge (McGill University, david.buckeridge@mcgill.ca); John S. Brownstein (Boston Children’s Hospital, Harvard University, john_brownstein@harvard.edu); Byron C. Wallace (Northeastern University, byron@ccs.neu.edu); Michael J. Paul (The University of Colorado Boulder, mpaul@colorado.edu)

Additional Information

W10 — Knowledge Extraction from Games

Knowledge Extraction from Games is a new workshop exploring questions of and approaches to the mechanical extraction of knowledge from games meant for humans &mdash including, but not limited to, game rules, character graphics, environment maps, music and sound effects, high-level goals or heuristic strategies, transferrable skills, aesthetic standards and conventions, or abstracted models of games.

Topics

Some examples of work that would be appropriate for KEG include contextual query-answering in games where nonplayer characters (or visual cues in environment design) offer hints to solve problems; extracting architectural information from game level layouts; transfer learning, analogical reasoning, or goal reasoning within or between games or game levels; game-playing agents which can explain their own actions or policy in terms of the game’s rules; learning the rules of a game from observation, or learning higher-level rules or goals automatically; or determining a designer or player’s mental model of game rules, and whether that differs from the rules induced by the game’s implementation.

We are especially keen to receive submissions from game designers or game critics on potentially mechanizable formalisms for knowledge representation and reasoning. We also welcome (especially in the short paper format) surveys or reframings of existing work in related fields reoriented towards (video) games.

Format

The workshop will be a series of thematically grouped presentations followed by brief discussions, with longer small-group discussions in the afternoon.

Attendance

Attendance is open to all; at least one author of each accepted submission must be present at the workshop.

Submissions

The workshop accepts two types of papers, all in AAAI format (references are not counted against page limits). Full papers are up to 6 pages and are expected to be accompanied by some evaluation or formal proof; short papers are between 3 and 4 pages, showing promising new directions, nascent ideas, or new applications of existing work. All submissions will be reviewed double-blind, so please take care to anonymize your submission.

Papers should be submitted via EasyChair.

Chair

Joseph C Osborn, University of California, Santa Cruz
(jcosborn@ucsc.edu)
Phone: +1 (585) 705 6309

Organizing Committee

Adam Summerville (University of California, Santa Cruz, asummerv@ucsc.edu), Matthew Guzdial (Georgia Institute of Technology, mguzdial3@gatech.edu)

Additional Information

W11 — Plan, Activity, and Intent Recognition

Plan recognition, activity recognition, and intent recognition all involve making inferences about other actors from observations of their behavior, that is, their interaction with the environment and with each other. The observed actors may be software agents, robots, or humans. This synergistic area of research combines and unifies techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multiagent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including: assistive technology, software assistants, computer and network security, behavior recognition, coordination in robots and software agents, and more.

This workshop seeks to bring together researchers and practitioners from diverse backgrounds, to share in ideas and recent results. It will aim to identify important research directions, opportunities for synthesis and unification of representations and algorithms for plan recognition.

This year’s workshop will be centered on application domains. This will include a focused discussion where we will present and compare the various representations common in the literature and applications. We believe this will work to help identify areas of synergy between different communities and to provide opportunities and incentives for future work.

Format

This 1-day workshop will be split 50/50 between research presentations, organized into topical sessions (topics to be decided based on submissions) and additional contents including two invited talks and a discussion panel.

Submissions

All submissions should be submitted to the EasyChair submission site. All papers must be original. If a work was submitted to the main conference as well, it should be written in the title. We accept full paper submissions. Papers must be formatted in AAAI two-column, camera-ready style; see the 2018 AAAI Author Kit for details. Papers must be in trouble-free, high-resolution PDF format, formatted for US Letter (8.5″ x 11″) paper, using Type 1 or TrueType fonts. Submissions are anonymous, and must conform to the AAAI-18 instructions for double-blind review. Submissions may have up to 6 pages with page 6 containing nothing but references. The last page of final papers may contain text other than references, but all references in the submitted paper should appear in the final version, unless superseded.

Organizing Committee

Ms. Reuth Mirsky
Ben-Gurion University
Department of Software and Information Systems Engineering
POB 15050, Be’er Sheva, 8412001, Israel
Email: dekelr@post.bgu.ac.il

Ms. Sarah Keren
Faculty of Industrial Engineering and Management
Technion – Israel Institute of Technology
Technion City, Haifa 32000, Israel
Email: sarahn@technion.ac.il

Dr. Christopher Geib
Drexel University, College of Computing and Informatics
3141 Chestnut St., Philadelphia, PA 19104 USA
Email: cgeib@drexel.edu

Additional Information

W012 — Planning and Inference

The workshop is focused on the problems of Stochastic Planning and Probabilistic Inference and the intimate connections between them. Both Planning and inference are core tasks in AI and the connections between them have been long recognized. However, much of the work in these subareas is disjoint. The last decade has seen many exciting developments with explicit constructions and reductions between planning and inference that aim for efficient algorithms for large scale problems and applications. The work in this area is distributed across many conferences, sub-communities, and sub-topics and varies from discrete to continuous problems, single versus multiagent problems, general versus spatial problems, propositional versus relational problems, model based planning versus reinforcement learning, and exact/optimal versus approximate versus heuristic solutions. Applications similarly vary for example from scheduling to sustainability and to robot control.

The goal of this workshop is to bring together researchers from all these areas and facilitate synergy and exchange of ideas: to discuss core ideas, techniques and algorithms that take advantage of the connection between planning and inference, identify opportunities and challenges for future work, and explore applications and how they can inform the development of such work.

The workshop will include invited talks by experts on planning and inference, contributed talks and a poster session, leaving room for discussion and interaction among participants. Current invited speakers include Rina Dechter, Marc Toussaint, and Pascal Van Hentenryck.

The workshop topic is broad and the intention of this first workshop is to enable interaction among the various sub-areas while keeping the focus on the interaction between planning and inference. Some basic questions include the following:

What are effective reductions from planning to inference?
What are effective inference algorithms for such problems?
What are the challenges in planning applications, and how does their structure help or interfere with the application of planning as inference?
Can generic inference algorithms be used directly for planning? or are we better off tailoring algorithms directly to the planning problem?
Can planning algorithms or ideas developed for them be used for general inference?
How do structured solutions, for example, lifted inference, lifted planning, spatial MDPs, cooperative multiagent systems, and approximations in continuous problems, translate across the planning/inference spectrum, and help improve scalability.
Success stories and challenges in using planning for inference or inference for planning.
These questions cut across theoretical foundations and practical applications.

Submissions

We invite 4 types of submissions (in AAAI style):

Papers describing work in progress (up to 8 pages including references).
Review of mature work (from multiple papers) by the authors (up to 8 pages including references).
Papers recently published at other venues (1-page abstract with a link to the full paper).
Position papers (2 pages including references).

All papers should clearly explain how the work relates planning and inference.

Submissions of papers being reviewed for AAAI 2018, or at other venues are welcome since this is a nonarchival venue (and if published they can be replaced with a 1 page abstract). If such papers are currently under blind review, please anonymize the submission.

Submissions should be made to the EasyChair submission site. Additional information is available at the workshop website.

Organizing Committee

Roni Khardon (Tufts University, USA), Akshat Kumar (Singapore Management University, Singapore), Alex Ihler (University of California, Irvine, USA)

Program Committee

Christopher Amato, Alan Fern, Vaishak Belle, Kristian Kersting, Qiang Liu, Radu Marinescu, Denis Maua, Sriraam Natarajan, Gerhard Neumann, Pascal Poupart, David Poole, Regis Sabbadin, Scott Sanner, Prasad Tadepalli, Jan-Willem van de Meent

Additional Information

W13 — Preference Handling

Preferences are a central concept of decision making. As preferences are fundamental for the analysis of human choice behavior, they are becoming of increasing importance for computational fields such as artificial intelligence, databases, and human-computer interaction as well as for their respective applications.

The workshop will provide a forum for presenting advances in preference handling and for exchanging experiences between researchers facing similar questions, but coming from different fields.

Topics

The workshop on Advances in Preference Handling addresses all computational aspects of preference handling. This includes methods for the elicitation, learning, modeling, representation, aggregation, and management of preferences and for reasoning about preferences. The workshop studies the usage of preferences in computational tasks from decision making, database querying, web search, personalized human-computer interaction, personalized recommender systems, e-commerce, multiagent systems, game theory, social choice, combinatorial optimization, planning and robotics, automated problem solving, perception and natural language understanding and other computational tasks involving choices. The workshop seeks to improve the overall understanding of and best methodologies for preferences in order to realize their benefits in the multiplicity of tasks for which they are used. Another important goal is to provide cross-fertilization between the numerous subfields that work with preferences.

Preference handling in artificial intelligence
Preference handling in database systems
Preference handling in multiagent systems
Applications of preferences
Preference elicitation
Preference representation and modeling
Properties and semantics of preferences
Practical preferences

Format

The program will consist of presentations of peer-reviewed papers, panel discussions about future challenges, and an invited talk.

Attendance

At least one author from each accepted paper must register for the workshop. An attendance of approximately 40 people is expected. All authors as well as researchers interested in the field are encouraged to participate.

Submissions

Papers must be formatted according to the AAAI Formatting Instructions and up to 6 pages in length + 1 page for references in PDF format. Authors can choose between an anonymized or nonanonymized submission. Authors should submit to the EasyChair submission site.

Chair

Kristen Brent Venable
Department of Computer Science, Tulane University
6823 St. Charles Avenue, New Orleans LA 70118, (USA)
E-mail: kvenabl@tulane.edu | Phone: +1 214 364 0739

Organizing Committee

Markus Endres, University of Augsburg (Germany), endres@informatik.uni-augsburg.de Nicholas Mattei, Cognitive Computing, IBM Research, n.mattei@ibm.com Andreas Pfandler, TU Wien (Austria) and University of Siegen (Germany), pfandler@dbai.tuwien.ac.at

Additional Information

W14 — Reasoning and Learning for Human-Machine Dialogues

Natural conversation is a hallmark of intelligent systems. Unsurprisingly, dialog systems have been a key sub-area of AI for decades. Their most recent form, chatbots, which can engage people in natural conversation and are easy to build in software, have been in the news a lot lately. There are many platforms to create dialogs quickly for any domain based on simple rules. Further, there is a mad rush by companies to release chatbots to show their AI capabilities and gain market valuation. However, beyond basic demonstration, there is little experience in how they can be designed and used for real-world applications needing decision making under constraints (for example, sequential decision making). The workshop will thus be timely to help chatbots realize their full potential.

Furthermore, there is upcoming interest and need for innovation in human-technology-interaction as addressed in the context of companion technology. Here, the aim is to implement technical systems that smartly adapt their functionality to their users’ individual needs and requirements and are even able to solve problems in close co-operation with human users. To this end, they need to enter into a dialog and should be able to convincingly explain their suggestions and their decision making behavior.

From research side, statistical and machine learning methods are well entrenched for language understanding and entity detection. However, the wider problem of dialog management is unaddressed with mainstream tools supporting rudimentary rule-based processing. There is an urgent need to highlight the crucial role of reasoning methods, like constraints satisfaction, planning and scheduling, and learning working together with them, can play to build an end-to-end conversation system that evolves over time. From practical side, conversation systems need to be designed for working with people in a manner that they can explain their reasoning, convince humans about choices among alternatives, and can stand up to ethical standards demanded in real life settings.

With these motivations, some areas of interest for the workshop (but not limited to) are as follows:

Dialog Systems

Early experiences with implemented dialog systems
Evaluation of dialog systems, metrics
Open domain dialog and chat systems
Task-oriented dialogs
Style, voice and personality in spoken dialogue and written text
Novel methods for NL generation for dialogs

Reasoning

Domain model acquisition, especially from unstructured text
Plan recognition in natural conversation
Planning and reasoning in the context of dialog systems

Learning

Learning to reason
Learning for dialog management
End2end models for conversation
Explaining dialog policy

Practical considerations

Ethical issues with reasoning in dialog systems
Corpora, tools and methodology for dialogue systems

The intended audience includes students, academic researchers and practitioners with an industrial background from the AI subareas of dialog systems, natural language processing, learning, reasoning, planning, HCI, ethics and knowledge representation.

Submissions

Papers must be formatted in AAAI two-column, camera-ready style. Regular research papers may be no longer than 7 pages, where page 7 must contain only references, and no other text whatsoever. Short papers, which describe a position on the topic of the workshop or a demonstration/tool, may be no longer than 4 pages, references included.

Organizing Committee

Biplav Srivastava (IBM Research, USA), Susanne Biundo (University of Ulm, Germany), Ullas Nambiar (Zensar Labs, India), Imed Zitouni (Microsoft AI+R, USA)

Additional Information

W15 — SmartIoT: AI Enhanced IoT Data Processing for Intelligent Applications

Building intelligent applications for everyday use is the long-cherished aim of Artificial Intelligence (AI). With numerous devices deployed and used in day-to-day applications including mobile phones, tablets, wearable and other connected sensing and actuation devices, collectively referred to as the Internet of Things (IoT), there is an unprecedented opportunity to develop contextually intelligent applications with far-reaching societal implications. They can deliver fine-grained services in various areas such as healthcare, manufacturing, transportation and social good. These intelligent applications and services, however, could also pose privacy, security and trust issues and risks.

The purpose of the workshop is to discuss how AI techniques can help consume data from IoT to build intelligent applications. The workshop aims to bring together academic researchers and industry practitioners who are interested in advancing the state-of-the-art not merely in their specific subfields of AI, but also in multidisciplinary areas in order to solve problems with business as well as societal impacts.

Topics

By 2020, 50 billion Internet of Things (IoT) are expected to be deployed. That is, massive number of IoTs will be continuously or periodically make the data the generate available on the internet. This workshop will explore the possibilities of using Artificial Intelligence (AI) including machine learning, NLP, and semantic Web techniques to create new solutions that exploit the IoT generated data. The workshop will provide an opportunity to share new findings, exchange ideas, discuss research challenges, present demonstration of unique applications and report latest findings. The workshop will cover a range of topics including, but not limited to the following:

Semantic sensor (IoT) data
Information extraction from real-world data streams
Machine Learning, knowledge-enabled and spatio-temporal processing applied IoT data
AI techniques for intelligent IoT data fusion
Context-aware applications and services
Linked open IoT data, repositories of semantic IoT data
Semantic IoT data management
Chatbots using NLP and IoT data
Reasoning with IoT data, including semantic, cognitive and perceptual computing
Ethical and privacy issues with IoT data
Human computer interface (HCI) issues, data visualization
Applications in smart city, healthcare, transportation, energy, public safety, disaster coordination and other areas

Submissions

Cochairs

Payam Barnaghi (University of Surrey, UK), Amelie Gyrard (Univ Lyon, MINES Saint-Etienne, CNRS, Laboratoire Hubert Curien, Saint-Etienne, France), Amit Sheth (Kno.e.sis, Wright State University, USA), Biplav Srivastava (IBM, USA)

Program Committee

Manfred Hauswirth (Technical University of Berlin/Fraunhofer FOKUS), Monika Solanki (University of Oxford, UK), Septimiu Nechifor (Siemens, Romania), Andreas Emrich (DFKI/University of Saarbrucken, Germany), Maria Bermudez (University of Granada, Spain), Frieder Ganz (Adobe, Germany), Cory Henson (Bosch Research & Technology, USA), Paolo Bellavista (University of Bologna, Italy), Ajit Joakar (City of London, UK), Edith Ngai (Uppsala University, Sweden), Fangming Liu (Huazhong University of Science and Technology, China), Yasmin Fathy (University of Surrey, UK), Danh Le Phuoc (TU Berlin, Germany), Josiane Xavier Parreira (SIEMENS AG, Austria), Maria Esther Vidal (Universidad Simon Bolivar, Venezuela), Simon Mayer (Siemens, USA), Pankesh Patel (Fraunhofer, USA), Ali Intizar (Insight-NUIG, Ireland), Gyu Myoung Lee (Liverpool John Moores University, UK), Emil Lupu (Imperial College London, UK), Bin Guo (Northwestern Polytechnical University, China), Koji Zettsu (NICT, Japan), Kerry Taylor (The Australian National University, Australia), Axel Ngonga (University of Leipzig, Germany), Xiang Su (University of Oulu, Finland), Philippe Gautier (Pierre and Marie Curie University, France)

Additional Information

Workshop URL
Email: smartiot2018@easychair.org

W16 — Statistical Modeling of Natural Software Corpora

The proliferation of open-source projects has led to large amounts of source code and related artifacts: arguably, the rich and open resources associated with software — including open source repositories, Q/A sites, change histories, and communications between developers — are the richest and most detailed information resource for any technical area. Recently it has been discovered that natural, human-produced software has many interesting statistical regularities. As a consequence, code corpora, just like natural language corpora, are amenable to statistical modeling, and a number of software tasks such as coding, testing, porting, bug-patching and others are potentially enhanced by the use of these statistical models.

Topics

This interdisciplinary workshop will explore issues related to the statistical modeling of software corpora, including topics such as modeling repetitiveness in source code; use of language models for the code suggestion in IDEs; using probabilistic grammars to mine programming idioms; statistical methods for type inference in a dynamically typed languages; statistical machine translation for porting applications between programming languages, or minifying Javascript; using statistical language models to find bugs; or statistical methods for automatic code patching, code summarization, code retrieval, code annotation, or test generation.

The workshop follows several earlier workshops on this topic at Microsoft Research, Dagstuhl event, and SIGSOFT FSE. From NSF, some funding is available for US travelers to the workshop, especially students and members of under-represented groups, and researchers that might not normally attend AAAI.

Submissions

We invite 2-4 page position paper submissions. Submissions will be reviewed by the workshop committee. Please submit to the EasyChair submission site.

Additional Information