It is well known that the title of an article impacts how well it is discovered by potential readers and read. With both people and search engines, acting on behalf of people, accessing papers from digital libraries, it is important that the paper titles should promote discovery. In this paper, we investigate the characteristics of titles of AI papers and then propose au- tomatic ways to augment them so that they can be better in- dexed and discovered by users. A user study with researchers shows that they overwhelmingly prefer the augmented titles over the originals for being more helpful.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities -- Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

List of organizers of the Cognitive Computing for Augmented Human Intelligence AAAI-14 Workshop.

In this paper, we describe the D-SCRIBE process used to build ontologies that are expected to have significant domain expansion after their initial introduction and whose coverage of concepts needs to be validated for a series of related applications. This process has been used to build SCRIBE, a very modular, ambitious ontology for the information about events triggered by both humans or nature, response activities by agencies that provide public services in cities by using resources and assets (land parcels, buildings, vehicles, equipment) and their communication (requests, work orders, sensor reports). SCRIBE reuses concepts from previously existing ontologies and data exchange standards, and D-SCRIBE retains traceability to these source influences.

Improving public health is a major responsibility of any government, and is of major interest to citizens and scientific communities around the world. Here, one sees two extremes. On one hand, tremendous progress has been made in recent years in the understanding of causes, spread and remedies of common and regularly occurring diseases like Dengue, Malaria and Japanese Encephalistis (JE). On the other hand, public agencies treat these diseases in an ad hoc manner without learning from the experiences of previous years. Specifically, they would get alerted once reported cases have already arisen substantially in the known disease season, reactively initiate a few actions and then document the disease impact (cases, deaths) for that period, only to forget this learning in the next season. However, they miss the opportunity to reduce preventable deaths and sickness, and their corresponding economic impact, which scientific progress could have enabled. The gap is universal but very prominent in developing countries like India.   In this paper, we show that if public agencies provide historical disease impact information openly, it can be analyzed with statistical and machine learning techniques, correlated with best emerging practices in disease control, and simulated in a setting to optimize social benefits to provide timely guidance for new disease seasons and regions. We illustrate using open data for mosquito-borne communicable diseases; published results in public health on efficacy of Dengue control methods and apply it on a simulated typical city for maximal benefits with available resources. The exercise helps us further suggest strategies for new regions that may be anywhere in the world, how data could be better recorded by city agencies and what prevention methods should medical community focus on for wider impact.

The AAAI-12 Workshop program was held Sunday and Monday, July 22–23, 2012 at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included 9 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, AI for Data Center Management and Cloud Computing, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, Semantic Cities. This article presents short summaries of those events.

We will like to call cities that enable such capabilities as, "semantic cities." In a semantic city, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Enabling city information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem could drive significant benefits and opportunities. Data (and then information and knowledge) from people, systems, and things is the single most scalable resource available to city stakeholders to reach the objective of semantic cities. Two major trends are supporting semantic cities -- open data and semantic web.

The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.