Knowledge-based planning methods offer benefits over classical techniques, but they are time consuming and costly to construct. There has been research on learning plan knowledge from search, but this can take substantial computer time and may even fail to find solutions on complex tasks. Here we describe another approach that observes sequences of operators taken from expert solutions to problems and learns hierarchical task networks from them. The method has similarities to previous algorithms for explanation-based learning, but differs in its ability to acquire hierarchical structures and in the generality of learned conditions. These increase the method's capability to transfer learned knowledge to other problems and supports the acquisition of recursive procedures. After presenting the learning algorithm, we report experiments that compare its abilities to other techniques on two planning domains. In closing, we review related work and directions for future research.

Abstract. We describe a framework for generating agent programs that model expert task performance in complex dynamic domains, using expert behavior observations and goal annotations as the primary source. We map the problem of learning an agent program on to multiple learning problems that can be represented in a "supervised concept learning" setting. The acquired procedural knowledge is partitioned into a hierarchy of goals and it is represented with first order rules. Using an inductive logic programming (ILP) learning component allows us to use structured goal annotations, structured background knowledge and structured behavior observations. We have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our system using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules.

We are proposing a new similarity based recommendation system for large-scale dynamic marketplaces. Our solution consists of an offline process, which generates long-term cluster definitions grouping short-lived item listings, and an online system, which utilizes these clusters to first focus on important similarity dimensions and next conducts a trade-off between further similarity and other quality factors such as seller trustworthiness. Our system generates these clusters from several hundred millions of item listings using a large Hadoop map-reduce based system. The clusters are learned using user queries as the main information source and therefore biased towards how users conceptually group items. Our system is deployed on several eBay sites in large-scale and has increased user-engagement and business metrics compared to the previous system. We show that utilizing user queries helps capturing similarity better. We also present experiments demonstrating that adapting the ranking function, which controls the trade-off between similarity and quality, to a specific context improves recommendation performance.

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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.