A British agritech start-up has won a prestigious Horizontal Innovation Award from the IET and the High Value Manufacturing Catapult (HVMC) to help develop'Harry', the company's drilling and planting robot. Small Robot Company, based in Shropshire, harnesses the power and precision of robots and Artificial Intelligence (AI) to improve the way that food is produced. The £50,000 funded research award will look to develop'Harry' from concept through to in-field prototype. Addressing key challenges around the use of robotics in agriculture, the development of'Harry's' punch planting mechanism will be supported by the Manufacturing Technology Centre, one of seven centres of excellence which make up the High Value Manufacturing Catapult (HVM Catapult), which is sponsored by Innovate UK. The technology is built on 15 years of robotics research by Professor Simon Blackmore, the world's leading expert on precision farming at Harper Adams University.
Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.
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.