Inappropriate prescribing of antimicrobials is a major clinical and health concern, as well as a financial burden, in hospitals worldwide. In this paper, we describe a deployed automated antimicrobial prescription surveillance system that has been assisting hospital pharmacists in identifying and reporting inappropriate antimicrobial prescriptions. One of the key characteristics of this system is its ability to learn new rules for detecting inappropriate prescriptions based on previous false alerts. The supervised learning algorithm combines instance-based learning and rule induction techniques. It exploits temporal abstraction to extract a meaningful time interval representation from raw clinical data, and applies nearest neighbor classification with a distance function on both temporal and non-temporal parameters. The learning capability is valuable both in configuring the system for initial deployment and improving its long term use. We give an overview of the application, point to lessons learned so far and provide insight into the machine learning capability.

Heuristic search is a fundamental technique for solving problems in artificial intelligence. However, many heuristic search algorithms, such as A* are limited by the amount of main memory available. External memory search overcomes the memory limitation of A* by taking advantage of cheap secondary storage, such as disk. Previous work in this area assumes that edge costs fall within a narrow range of integer values and relies on uniformed search order. The goal of this dissertation research is to develop novel techniques that enable heuristic search algorithms to solve problems with real values using a best-first search order while exploiting external memory and multiple processors. This work will be organized into four components. The first component will discuss external memory search and present a novel technique for incorporating real-valued edge costs. The second component will present a novel algorithm for solving problems with large branching factors with application to the challenging problem of Multiple Sequence Alignment (MSA). The third component will cover bounded suboptimal external search for practical MSA applications. The final component of this research will be the development of a novel distributed search framework; allowing parallel and external memory heuristic search algorithms to run cooperatively on a commodity computing cluster. Together these four components will enable heuristic search to scale to large problems in practical settings while exploiting modern hardware.

Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day.1 We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.

A key challenge to create a sustainable and energy-efficient society is in making consumer demand adaptive to energy supply, especially renewable supply. In this paper, we propose a partially-centralized organization of consumers, namely, a consumer cooperative for purchasing electricity from the market. We propose a novel multiagent coordination algorithm to shape the energy consumption of the cooperative. In the cooperative, a central coordinator buys the electricity for the whole group and consumers make their own consumption decisions based on their private consumption constraints and preferences. To coordinate individual consumers under incomplete information, we propose an iterative algorithm in which a virtual price signal is sent by the coordinator to induce consumers to shift demand. We prove that our algorithm converges to the central optimal solution. Additionally we analyze the convergence rate of the algorithm via simulations on randomly generated instances. The results indicate scalability with respect to the number of agents and consumption slots.

An attractive approach to managing electricity demand in the Smart Grid relies on real-time pricing (RTP) tariffs, where customers are incentivized to quickly adapt to changes in the cost of supply. However, choosing amongst competitive RTP tariffs is difficult when tariff prices change rapidly. The problem is further complicated when we assume that the price changes for a tariff are published in real-time only to those customers who are currently subscribed to that tariff, thus making the prices partially observable. We present models and learning algorithms for autonomous agents that can address the tariff selection problem on behalf of customers. We introduce 'Negotiated Learning', a general algorithm that enables a self-interested sequential decision-making agent to periodically select amongst a variable set of 'entities' (e.g., tariffs) by negotiating with other agents in the environment to gather information about dynamic partially observable entity 'features' (e.g., tariff prices) that affect the entity selection decision. We also contribute a formulation of the tariff selection problem as a 'Negotiable Entity Selection Process', a novel representation. We support our contributions with intuitive justification and simulation experiments based on real data on an open Smart Grid simulation platform.  

Sustainable energy systems of the future will need more than efficient, clean, and low-cost energy sources. They will also need efficient price signals that motivate sustainable energy consumption behaviors and a tight real-time alignment of energy demand with supply from renewable and traditional sources. The Power Trading Agent Competition (Power TAC) is a rich, competitive, open-source simulation platform for future retail power markets built on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making as well as the robustness of proposed market designs. Power TAC invites researchers to develop autonomous electricity broker agents and to pit them against best-in-class strategies in global competitions, the first of which will be held at AAAI 2013. Power TAC competitions provide compelling, actionable information for policy makers and industry leaders. We describe the competition scenario, demonstrate the realism of the Power TAC platform, and analyze key characteristics of successful brokers in one of our 2012 pilot competitions between seven research groups from five different countries.

We describe ongoing research towards building a cognitively plausible system for near one-shot learning of the meanings of attribute words and object names, by grounding them in a sensory model. The system learns incrementally from human demonstrations recorded with the Microsoft Kinect, in which the demonstrator can use unrestricted natural language descriptions. We achieve near-one shot learning of simple objects and attributes by focusing solely on examples where the learning agent is confident, ignoring the rest of the data. We evaluate the system's learning ability by having it generate descriptions of presented objects, including objects it has never seen before, and comparing the system response against collected human descriptions of the same objects. We propose that our method of retrieving object examples with a k-nearest neighbor classifier using Mahalanobis distance corresponds to a cognitively plausible representation of objects. Our initial results show promise for achieving rapid, near one-shot, incremental learning of word meanings.

Road travel costs are important knowledge hidden in large-scale GPS trajectory data sets, the discovery of which can benefit many applications such as intelligent route planning and automatic driving navigation. While there are previous studies which tackled this task by modeling it as a regression problem with spatial smoothness taken into account, they unreasonably assumed that the latent cost of each road remains unchanged over time. Other works on route planning and recommendation that have considered temporal factors simply assumed that the temporal dynamics be known in advance as a parametric function over time, which is not faithful to reality. To overcome these limitations, in this paper, we propose an extension to a previous static trajectory regression framework by learning the temporal dynamics of road travel costs in an innovative non-parametric manner which can effectively overcome the temporal sparsity problem. In particular, we unify multiple different trajectory regression problems in a multi-task framework by introducing a novel cross-task regularization which encourages temporal smoothness on the change of road travel costs. We then propose an efficient block coordinate descent method to solve the resulting problem by exploiting its separable structures and prove its convergence to global optimum. Experiments conducted on both synthetic and real data sets demonstrate the effectiveness of our method and its improved accuracy on travel time prediction.

Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds. The proposed approaches are based on the diffusion wavelets framework, data driven, and able to directly process directional neighborhood relationships without ad-hoc symmetrization. The proposed multiscale algorithms are evaluated using both synthetic and real-world data sets, and shown to outperform previous manifold learning methods.

Motivated by the problem of building a basic reasoner for general game playing with imperfect information, we address the problem of filtering with logic programs, whereby an agent updates its incomplete knowledge of a program by observations. We develop a filtering method by adapting an existing backward-chaining and abduction method for so-called open logic programs. Experimental results show that this provides a basic effective and efficient "legal" player for general imperfect-information games.