Much work in learning from demonstration has focused on learning simple tasks from structured demonstrations that have a well-defined beginning and end. As we attempt to scale robot learning to increasingly complex tasks, it becomes intractable to learn task policies monolithically. Furthermore, it is desirable to be able to learn from natural, unstructured demonstrations, which are unsegmented, possibly incomplete, and may come from different tasks. We propose a three-part approach to designing a natural, scalable system that allows a robot to learn tasks of increasing complexity by automatically building and refining a library of skills over time. First, we describe a Bayesian nonparametric model that can segment unstructured demonstrations into appropriate numbers of component skills and recognize repeated skills across demonstrations and tasks. These skills can then be parameterized and generalized to new situations. Second, we propose to create a system that allows the user to provide unstructured corrections and feedback to the robot, without requiring any knowledge of the robot's underlying representation of the task or its component skills. Third, we propose to infer the user's intentions for each segmented skill and autonomously improve these skills using reinforcement learning. This approach will be applied to learn and generalize complex, multi-step tasks that are beyond the reach of current LfD methods, using the PR2 mobile manipulator as a testing platform.

The structure of a Markov network is typically learned in one of two ways. The first approach is to treat this task as a global search problem. However, these algorithms are slow as they require running the expensive operation of weight (i.e., parameter) learning many times. The second approach involves learning a set of local models and then combining them into a global model. However, it can be computationally expensive to learn the local models for datasets that contain a large number of variables and/or examples. This paper pursues a third approach that views Markov network structure learning as a feature generation problem. The algorithm combines a data-driven, specific-to-general search strategy with randomization to quickly generate a large set of candidate features that all have support in the data. It uses weight learning, with L1 regularization, to select a subset of generated features to include in the model. On a large empirical study, we find that our algorithm is equivalently accurate to other state-of-the-art methods while exhibiting a much faster run time.

The majority of existing approaches to mobile robot mapping assumes that the world is static, which is generally not justified in real-world applications. However, in many navigation tasks including trajectory planning, surveillance, and coverage, accurate maps are essential for the effective behavior of the robot.  In this paper we present a probabilistic grid-based approach for modeling changing environments. Our method represents both, the occupancy and its changes in the corresponding area where the dynamics are characterized by the state transition probabilities of a Hidden Markov Model. We apply an offline and an online technique to learn the parameters from observed data. The advantage of the online approach is that it can dynamically adapt the parameters and at the same time does not require storing the complete observation sequences.  Experimental results obtained with data acquired by real robots demonstrate that our model is well-suited for representing changing environments. Further results show that our technique can be used to substantially improve the effectiveness of path planning procedures.

The presence of non-symmetric evidence has been a barrier for the application of lifted inference since the evidence destroys the symmetry of the first-order probabilistic model. In the extreme case, if distinct soft evidence is obtained about each individual object in the domain then, often, all current exact lifted inference methods reduce to traditional inference at the ground level. However, it is of interest to ask whether the symmetry of the model itself before evidence is obtained can be exploited. We present new results showing that this is, in fact, possible. In particular, we show that both exact maximum a posteriori (MAP) and marginal inference can be lifted for the case of distinct soft evidence on a unary Markov Logic predicate. Our methods result in efficient procedures for MAP and marginal inference for a class of graphical models previously thought to be intractable.

In conservation biology and natural resource management, adaptive management is an iterative process of improving management by reducing uncertainty via monitoring. Adaptive management is the principal tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a restricted Mixed Observability MDP called hidden model MDP (hmMDP). We demonstrate how to simplify the value function, the backup operator and the belief update computation. We show that, although a simplified case of POMDPs, hm-MDPs are PSPACE-complete in the finite-horizon case. We illustrate the use of this model to manage a population of the threatened Gouldian finch, a bird species endemic to Northern Australia. Our simple modelling approach is an important step towards efficient algorithms for solving adaptive management problems.

The multiagent systems community has adopted game theory as a framework for the design of systems of multiple self-interested agents. For this to be effective, efficient algorithms must be designed to compute the solutions that game theory prescribes. In this paper, I summarize some of the state of the art on this topic, focusing particularly on how this line of work has contributed to several highly visible deployed security applications, developed at the University of Southern California.

Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. However, these are intractable, losing the original motivation. Here we propose the first non-trivially tractable first-order probabilistic language. It is a subset of Markov logic, and uses probabilistic class and part hierarchies to control complexity. We call it TML (Tractable Markov Logic). We show that TML knowledge bases allow for efficient inference even when the corresponding graphical models have very high treewidth. We also show how probabilistic inheritance, default reasoning, and other inference patterns can be carried out in TML. TML opens up the prospect of efficient large-scale first-order probabilistic inference.

A key problem in climate science is how to combine the predictions of the multi-model ensemble of global climate models. Recent work in machine learning (Monteleoni et al. 2011) showed the promise of an algorithm for online learning with experts for this task.We extend the Tracking Climate Models (TCM) approach to (1) take into account climate model predictions at higher spatial resolutions and (2) to model geospatial neighborhood influence between regions. Our algorithm enables neighborhood influence by modifying the transition dynamics of the Hidden Markov Model used by TCM, allowing the performance of spatial neighbors to influence the temporal switching probabilities for the best expert (climate model) at a given location. In experiments on historical data at a variety of spatial resolutions, our algorithm demonstrates improvements over TCM, when tracking global temperature anomalies.

Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e.g., labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.

To ensure quality results from unreliable crowdsourced workers, task designers often construct complex workflows and aggregate worker responses from redundant runs. Frequently, they experiment with several alternative workflows to accomplish the task, and eventually deploy the one that achieves the best performance during early trials. Surprisingly, this seemingly natural design paradigm does not achieve the full potential of crowdsourcing. In particular, using a single workflow (even the best) to accomplish a task is suboptimal. We show that alternative workflows can compose synergistically to yield much higher quality output. We formalize the insight with a novel probabilistic graphical model. Based on this model, we design and implement AGENTHUNT, a POMDP-based controller that dynamically switches between these workflows to achieve higher returns on investment. Additionally, we design offline and online methods for learning model parameters. Live experiments on Amazon Mechanical Turk demonstrate the superiority of AGENTHUNT for the task of generating NLP training data, yielding up to 50% error reduction and greater net utility compared to previous methods.