Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system's component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements.

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

A geoscientist would be faced with the situation shown on the right of the figure; his task is to deduce the situation shown at the left, along with the processes that were at work and the timeline involved. To accomplish this, a geoscientist first dates a set of rock samples from the present surface, then reasons backward to deduce what process affected the original landform. This is a difficult deduction: geological processes take place over an extremely long period of time, and evidence remaining today is scarce and noisy. Finally, experts in geological dating, like experts in any field, are only human, and can be biased in favor of one theory over another. In the face of these problems, experts form an exhaustive list of possible hypotheses and consider the evidence for and against each one--much like the AI concept of argumentation. Our system to automate this reasoning, Calvin, uses the same argumentation process as experts, comparing the strength of the evidence for and against a set of hypotheses before coming to a conclusion. We collected knowledge about how isotope dating experts reason through interviews with several dozen geoscientists.

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.

A common problem for companies with strong business growth is that it is hard to find enough experienced staff to support expansion needs. This problem is particular pronounced for operations planners and controllers who must be very highly knowledgeable and experienced with the business domain. This article is a case study of how one of the largest travel agencies in Hong Kong alleviated this problem by using AI to support decision-making and problem-solving so that their planners and controllers can work more effectively and efficiently to sustain business growth while maintaining consistent quality of service. AI is used in a mission critical fleet management system (FMS) that supports the scheduling and management of a fleet of luxury limousines for business travelers. The AI problem was modeled as a constraint satisfaction problem (CSP). The use of AI enabled the travel agency to sign up additional hotel partners, handle more orders and expand their fleet with their existing team of planners and controllers. Using modern web 2.0 architecture and proven AI technology, we were able to achieve low-risk implementation and deployment success with concrete and measurable business benefits.

We are proud to continue this tradition with the presentation of five articles from the Twenty Second IAAI conference that was held in Atlanta, Georgia, from July 11-14, 2010. We were especially honored to have Jay M. (Marty) Tenenbaum accept the Robert S. Engelmore Memorial Award for his exceptional contributions to AI in computer vision and manufacturing as well as his visionary role in the birth of electronic commerce. This issue of AI Magazine includes an article based on his lecture Cancer: A Computational Disease That AI Can Cure. In this article, Jay Tenenbaum and Jeff Shrager provide a personal view of their work in the development of an AIbased system that addresses the challenge of helping to find a cure for cancer. As a cancer survivor himself, Tenenbaum has a unique insight into the shortcomings of current approaches to treating this disease.

We consider the problem of training probabilistic conditional random fields (CRFs) in the context of a task where performance is measured using a specific loss function. While maximum likelihood is the most common approach to training CRFs, it ignores the inherent structure of the task's loss function. We describe alternatives to maximum likelihood which take that loss into account. These include a novel adaptation of a loss upper bound from the structured SVMs literature to the CRF context, as well as a new loss-inspired KL divergence objective which relies on the probabilistic nature of CRFs. These loss-sensitive objectives are compared to maximum likelihood using ranking as a benchmark task. This comparison confirms the importance of incorporating loss information in the probabilistic training of CRFs, with the loss-inspired KL outperforming all other objectives.

Constraint propagation is one of the basic forms of inference in many logic-based reasoning systems. In this paper, we investigate constraint propagation for first-order logic (FO), a suitable language to express a wide variety of constraints. We present an algorithm with polynomial-time data complexity for constraint propagation in the context of an FO theory and a finite structure. We show that constraint propagation in this manner can be represented by a datalog program and that the algorithm can be executed symbolically, i.e., independently of a structure. Next, we extend the algorithm to FO(ID), the extension of FO with inductive definitions. Finally, we discuss several applications.

The theoretical limits of'lossy' data compression algorithms are considered. The complexity of an object as seen by a macroscopic observer is the size of the perceptual code which discards all information that can be lost without altering the perception of the specified observer. The complexity of this macroscopically observed state is the simplest description of any microstate comprising that macrostate. Inference and pattern recognition based on macrostate rather than microstate complexities will take advantage of the complexity of the macroscopic observer to ignore irrelevant noise. Information theory in its modern form originated from Claude Shannon's[22] usage of Gibbs' entropy formula to describe communication channels: S k P In the context of quantum mechanics, it becomes the von Neumann entropy of the state density matrix, S trace(plogp). The story goes that it was actually von Neumann who suggested the term'entropy' to Shannon for his information function, for two reasons: 'In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, nobody knows what entropy really is, so in a debate you will always have the advantage.'

In this paper we present a lossless technique to compress pattern databases (PDBs) in the Pancake Sorting problems. This compression technique together with the choice of zero-cost operators in the construction of additive PDBs reduces the memory requirement for PDBs in these problems to a great extent, thus making otherwise intractable problems able to be efficiently handled. Also, using this method, we can construct some problem-size independent PDBs. This precludes the necessity of constructing new PDBs for new problems with different numbers of pancakes. In addition to our compression technique, by maximizing over the heuristic value of additive PDBs and the modified version of the gap heuristic, we have obtained powerful heuristics for the burnt pancake problem.