Developing agents that could perceive the world, reason about what they perceive in relation to their own goals and acts, has been the Holy Grail of AI. Early attempts at such holistic intelligence (for example, SRI International's AI researchers turned their attention to component technologies for structuring a single agent, such as planning, knowledge representation, diagnosis, and learning. Although most of AI research was focused on single-agent issues, a small number of AI researchers gathered at the Massachusetts Institute of Technology Endicott House in 1980 for the First Workshop on Distributed AI. The main scientific goal of distributed AI (DAI) is to understand the principles underlying the behavior of multiple entities in the world, called agents and their interactions. The discipline is concerned with how agent interactions produce overall multiagent system (MAS) behavior.
As evidenced by the articles in this special issue, transfer learning has come a long way in the past five or so years, partially because of DARPA's Transfer Learning program, which sponsored much of the work reported in this issue. There is a Transfer Learning Toolkit for Matlab available on the web. Transfer learning has developed techniques for classification, regression, and clustering (as summarized in Pan and Yang's 2009 survey) and for complex interactive tasks that are often best addressed by reinforcement learning techniques. However, there is a more practical and more feasible goal for transfer learning against which progress is being made. An engineering-oriented goal of artificial intelligence that could be enabled by transfer learning is the ability to construct a large number of diverse applications not from scratch, but by taking advantage of knowledge already acquired and formally represented for other purposes.
Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain. A Note from the AI Magazine Editor in Chief: Part Two of the Structured Knowledge Transfer special issue will be published in the summer 2011 issue (volume 32 number 2) of AI Magazine. Articles in this issue will include: "Knowledge Transfer between Automated Planners," by Susana Fernández, Ricardo Aler, and Daniel Borrajo "Transfer Learning by Reusing Structured Knowledge," by Qiang Yang, Vincent W. Zheng, Bin Li, and Hankz Hankui Zhuo "An Application of Transfer to American Football: From Observation of Raw Video to Control in a Simulated Environment," by David J. Stracuzzi, Alan Fern, Kamal Ali, Robin Hess, Jervis Pinto, Nan Li, Tolga Könik, and Dan Shapiro "Toward a Computational Model of Transfer," by Daniel Oblinger While the field of psychology has studied transfer learning in people for many years, AI has only recently taken up the challenge. The topic received initial attention with work on inductive transfer in the 1990s, while the number of workshops and conferences has noticeably increased in the last five years. This special issue represents the state of the art in the subarea of transfer learning that focuses on the acquisition and reuse of structured knowledge.
The research addressed in the autonomous agents field covers a wide spectrum of levels from the cognitive to the organizational, exploits diverse mechanisms and approaches, and has had a major impact on many aspects of artificial intelligence research. In 2011 the Autonomous Agents and Multiagent Systems (AAMAS) conference series celebrated its 10th anniversary, having begun as the successful merger of three related events that had run for some years previously. The 2011 AAMAS conference received 575 submissions, and 126 papers were selected for publication as full papers. Representation under all submissions of topics (measured by first keyword) was broad, with top counts in areas such as teamwork, coalition formation, and coordination (31), distributed problem solving (30), game theory (30), planning (26), multiagent learning (24), and trust, reliability, and reputation (17). The tag cloud (figure 1), generated from the titles of the full papers at the conference, conveys a sense of the relative prominence of topics.
Semantic web technologies (Hitzler, Krötzsch, and Rudolph 2010) are meant to deal with these issues, and indeed since the advent of linked data (Bizer, Heath, and Berners-Lee 2009) a few years ago, they have become central to mainstream semantic web research and development. We can easily understand linked data as being a part of the greater big data landscape, as many of the challenges are the same (Hitzler and Janowicz 2013). The linking component of linked data, however, puts an additional focus on the integration and conflation of data across multiple sources. This issue of AI Magazine is a followup from that meeting and contains significantly extended, enhanced, and updated contributions. We summarize the articles in the following paragraphs.
This meeting represented the most significant transformation in the history of IAAI. IAAI-97 consisted of two paper tracks as well as invited talks and panels. The first paper track, Deployed-Application Case Studies, comprised papers about deployed AI systems that are relied on for operations and have clearly defined business value. This track was equivalent to previous IAAI programs. The deployed applications track's standards for innovation recognize four types: (1) first application of an AI technique in a deployed application, (2) application of an AI technique to a new domain, (3) a high business payoff, and (4) a novel integration of techniques.
Recent years have witnessed significant progress in intelligent user interfaces. Emerging from the intersection of AI and human-computer interaction, research on intelligent user interfaces is experiencing a renaissance, both in the overall level of activity and in raw research achievements. Because intelligent user interfaces are designed to facilitate problem-solving activities where reasoning is shared between users and the machine, they are currently transitioning from the laboratory to applications in the workplace, home, and classroom. Most of these projects have been presented at the premiere forum for intelligent user interface (IUI) research, the International Conference on Intelligent User Interfaces. First, they describe projects that explore the boundaries of the man-machine interface.
This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of three articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, military operations, and citizen science. The 2015 conference continued the tradition with a selection of 6 deployed applications describing systems in use by their intended end users, 13 emerging applications describing works in progress, and three papers in a new category for challenge problems. In the first article, Activity Planning for a Lunar Orbital Mission, John Bresina describes a deployed application of current planning technology in the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer).
We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, and citizen science. The 2014 conference continued the tradition with a selection of 7 deployed applications describing systems in use by their intended end users, and 14 emerging applications describing works in progress. This year's special issue on innovative applications features articles describing four deployed and two emerging applications. The articles include three different types of recommender systems, which may be as much of a critique of the role of technology in society as it is an indication of recent research trends.
We are very pleased to republish here extended versions of a sample of the papers drawn from the Innovative Applications of Artificial Intelligence Conference (IAAI-06), which was held July 17-20, 2006, in Boston, Massachusetts. Three of these articles describe deployed applications and two describe emerging applications. By this measure, artificial intelligence is going strong. Evidence comes from the annual Conference on Innovative Applications of Artificial Intelligence (IAAI), the premier conference on AI applications. The papers presented at the conference provide compelling case studies of the value and impact of AI technology.