IAAI 2016 : Innovative Applications of Artificial Intelligence
Conference Series : Innovative Applications of Artificial Intelligence
Call For Papers
The Twenty-Eighth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-16) will focus on successful applications of AI technology. The conference will use technical papers, challenge papers, invited talks, and panel discussions to explore issues, methods, and lessons learned in the development and deployment of AI applications; and to promote an interchange of ideas between basic and applied AI.
IAAI-16 will consider papers in three tracks: (1) deployed application case studies, (2) emerging applications or methodologies, and (3) challenge problem papers. Submissions should clearly identify which track they are intended for, as the three tracks are judged on different criteria. All submissions must be original.
DEPLOYED APPLICATION CASE STUDY PAPERS
Case-study papers must describe deployed applications with measurable benefits that include some aspect of AI technology. Applications are defined as deployed once they are in production use by their final end-users (not the people who created the application) for sufficiently long that the in-use experience can be meaningfully collected and reported. This period typically spans at least three months. The case study may evaluate either a stand-alone application or a component of a complex system. In addition to the criteria listed below for Emerging Track papers, the deployed applications will also be evaluated on the following:
Task or Problem Description: Describe the task the application performs or the problem it solves. State the objectives of the application and explain why an AI solution was important.
Application Description: Describe the application, providing key technical details about design and implementation. What are the system components, what are their functions, and how do they interact? What languages and tools are used in the application? How is knowledge represented? What is the hardware and software environment in which the system is deployed? Provide examples to illustrate how the system is used.
Uses of AI Technology: On what AI research results does the application depend? What key aspects of AI technology allowed the application to succeed? How were the techniques modified to fit the needs of the application? If applicable, describe how AI technology is integrated with other technology. If a commercial tool is used, explain the decision criteria used to select it. Describe any insights gained about the application of AI technology. What AI approaches or techniques were tried and did not work? Why not? If other solutions were tried and failed outline these solutions and the reasons for their failure.
Application Use and Payoff: How long has this application been deployed? Explain how widely, how often, and by whom the application is being used. Also describe the application's payoff. What measurable benefits have resulted from its use? What additional benefits do you expect over time? What impacts has it had on the users' business processes?
Application Development and Deployment: Describe the development and deployment process. How long did they take? How many developers were involved? What were the costs? What were the difficulties, and how were they overcome? What are the lessons learned? What, if any, formal development methods and processes were used?
Maintenance: Describe your experience with and plans for maintenance of the application. Who maintains the application? How often does the application need to be updated? Is domain knowledge expected to change over time? How does the design of the application facilitate update?
Original papers on the deployment issues in AI applications are welcome, even if other papers on the AI technology have been presented at or submitted to other conferences. We strongly encourage updates on applications that have been in use for an extended period of time (that is, multiple years). These updates may include (but are not limited to) deployed applications that have been published at previous IAAI conferences with significant new learnings and experiences to report on or previously published emerging application papers that now met the requirements for a deployed application paper as outlined above. Each of the accepted deployed application papers will receive the IAAI "Innovative Application" Award.
EMERGING APPLICATION CASE STUDY PAPERS
The goal of the emerging application track is to "bridge the gap" between basic AI research and deployed AI applications, by discussing efforts to apply AI tools, techniques, or methods to real world problems. Emerging applications focus on aspects of AI applications that are not appropriate for deployed application case studies, or are not sufficiently deployed to be submitted as case studies. This track is distinguished from reports of scientific AI research appropriate for the AAAI-16 Conference in that the objective of the efforts reported here should be the engineering and application of AI technologies.
Emerging application papers may include any aspects of the technology, engineering, or deployment of AI applications, including discussions of prototype applications; performance evaluation of AI applications; ongoing efforts to develop large-scale or domain-specific knowledge bases or ontologies; development of domain or task focused tools, techniques, or methods; evaluations of AI tools, techniques or methods for domain suitability; unsuccessful attempts to apply particular tools, techniques or methods to specific domains (which shed insight on the applicability and limitations of the tool, technique or method); system architectures that work; scalability of techniques; integration of AI with other technologies; development methodologies; validation and verification; lessons learned; social and other technology transition issues.
The following questions will appear on the review form for emerging technology papers. Authors are advised to bear these questions in mind while writing their papers. Reviewers will look for papers that meet at least some (although not necessarily all) of the criteria in each category.
Significance: How important is the problem being addressed? Is it a difficult or simple problem? Is it central or peripheral to a category of applications? Is the tool or methodology presented generally applicable or domain specific? Does the tool or methodology offer the potential for new or more powerful applications of AI?
AI Technology: Does the paper identify AI research needed for a particular application or class of applications? Does the paper characterize the needs of application domains for solutions of particular AI problems? Does the paper evaluate the applicability of an AI tool or methodology for an application domain? Does the paper describe AI technology that could enable new or more powerful AI applications?
Innovation: Does the tool, technique, or method advance the state of the art or state of the practice of AI technology? Does the tool, technique, or method address a new or previously reported problem? If it is a previously reported problem, does the tool, technique, or method solve it in a different, new, more effective, or more efficient way? Does the reported work integrate AI with other AI or non-AI technologies in a new way? Does the work provide a new perspective on an application domain? Does the work apply AI to a new domain?
Content: Does the paper motivate the need for the tool or methodology? Does the paper adequately describe the task it performs or the problem it solves? Does it provide technical details about the design and implementation of the tool or methodology? Does the paper clearly identify the AI research results on which the tool or methodology depends? Does it relate the tool or methodology to the needs of application domains? Does it provide insights about the use of AI technology in general or for a particular application domain? Does it describe the development process and costs? Does it discuss estimated or measured benefits? Does it detail the evaluation method and results?
Evaluation: Has the tool or methodology been tested on real data? Has it been evaluated by end users? Has it been incorporated into a deployed application? Has it been compared to other competing tools or methods?
Technical Quality: Is the paper technically sound? Does it carefully evaluate the strengths and limitations of its contribution? Are the results described and evaluated? Are its claims backed up? Does it identify and describe relevant previous work?
Clarity: Is the paper clearly written? Is it organized logically? Are there sufficient figures and examples to illustrate the key points? Is the paper accessible to those outside the application domain? Is it accessible to those in other technical specialties?
CHALLENGE PROBLEM PAPERS
The goal of this track is to identify new opportunities and the associated research and development challenges in applying artificial intelligence to real world problems. IAAI seeks well-motivated and specific problems for which artificial intelligence technology may be well-positioned to improve either the state of the art (cutting-edge technology) or the state of the practice (methods currently used to solve the problem, which may fall significantly short of the state of the art). Challenge problems may include roadblocks to applying existing AI technology to new tasks, or fundamentally new technologies needed for new applications. These problems may consider any aspect of specific AI applications, including the underlying technology, engineering concerns, or technology transition and deployment issues. Importantly, challenge problem submissions should be grounded in reality. "What-if" scenarios that show no tie or insight to the application domain will not be accepted. We particularly welcome submissions from industry, government laboratories, and funding agencies that can provide a real world perspective to the challenge problems. (For an example of a previous challenge paper, please see Jeremiah T. Folsom-Kovarik's successful submission to a previous year's conference.)
The following questions will appear on the review form for challenge problem papers. Authors are advised to bear these questions in mind while writing their challenge papers.
Significance: How important is the proposed challenge problem? Is it a difficult or simple problem? Is it central or peripheral to a category of applications?
Innovation: Would a solution to the challenge problem advance the state of the art in AI technology or state of the practice in the application domain? Does the challenge problem require an integration of AI with other AI or non-AI technologies in a new way? Are solutions to the problem likely to provide a new perspective on an application domain? Does the problem require the application of AI to a new domain?
AI Technology: Does the paper characterize the AI research needed for a particular application or class of applications? Does the paper characterize the needs of application domains for solutions of particular AI problems?
Content: Does the paper motivate the challenge problem? Does the paper adequately describe the task or problem? Does it discuss the needs of the application domain? Does it provide insights about the potential use of AI technology in general or for a particular application domain?
Evaluation: Does the paper indicate a source for data relevant to the problem? Does the paper indicate how researchers might artificially generate data for the problem? Does the paper provide evaluation criteria for proposed solutions?
Clarity: Is the paper clearly written? Is the paper accessible to those outside the application domain? Is it accessible to those in other technical specialties?
As challenge problems span multiple years, we also encourage updates on challenge papers published at either previous IAAI conferences or other venues. In particular, updates involving significant progress on the original challenge problem that was reported; new innovations, methodologies, and technologies that have been developed or applied in response to the original challenge problem; and broader societal and technology trends that have impacted the original challenge problem are strongly encouraged.