Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions. Current tools either require significant levels of manual intervention (e.g., to prepare the data, to define patterns, or to draw conclusions from data) or are unable to generalize to new data sources and analysis needs. In this paper, we present automated solutions to two major problems in network analysis: (a) finding patterns in the network data that contains high levels of noise and irrelevant information; and (b) learning repetitive patterns and dependencies between entities and attributes. Our modeling framework represents network data using multi-attributed graphs that can encode various discrete and continuous features and relationships between network entities. The pattern search and learning model is based on probabilistic multi-attributed graph matching, and implemented using distributed message passing algorithms. Our algorithms achieved high accuracy rates in learning and finding patterns in the data, are flexible to new domains and data types, and scale to large datasets using the Map-Reduce framework.

There is growing interest in automating the detection of interesting new developments in science and technology. BAE Systems is pursuing ARBITER (Abductive Reasoning Based on Indicators and Topics of EmeRgence), a multi-disciplinary study and development effort to analyze full- text and metadata for indicators of emergent technologies and scientific fields. To define these indicators, our team has applied the primary insights of actant network theory developed within the disciplines of Science and Technology Studies and the history of technology and science to create a pragmatic theory of technoscientific emergence. Specifically, this practical theory articulates emergence in terms of the robustness of actant networks. This applied actant-network theory currently guides our definition of indicators and indicator patterns for the ARBITER system, and represents a novel contribution to the discussion of emergent technologies and fields. Several elements of our theory were validated with 15 case studies and 25 example technologies.

We consider the problem of extending manually trainedagents via evaluative reinforcement (TAMER) in con-tinuous state and action spaces. The early work TAMERframework allows a non-technical human to train anagent through a natural form of human feedback, neg-ative or positive. The advantages of TAMER havebeen shown on applications such as training Tetris andMountain Car with only human feedback, Cart-poleand Mountain Car with human feedback and environ-ment reward (augmenting reinforcement learning withhuman feedback). However, those methods are origi-nally designed for discrete state-action, or continuousstate-discrete action problems. In this paper, we intro-duce an extension of TAMER to allow both continu-ous states and actions. The new scheme, actor-criticTAMER, extends the original TAMER to allow usingany general function approximation of a human trainer’sreinforcement signal. Our extension still allows rein-forcement learning to be easily combined with humanfeedback. The experimental results show that the pro-posed method helps a human trainer successfully trainan agent in two continuous state-action domains: Moun-tain Car, and Cart-pole (balancing).

Members of a multi-robot team, operating within close quarters, need to avoid crashing into each other. Simple collision avoidance methods can be used to prevent such collisions, typically by computing the distance to other robots and stopping, perhaps moving away, when this distance falls below a certain threshold. While this approach may avoid disaster, it may also reduce the team's efficiency if robots halt for a long time to let others pass by or if they travel further to move around one another. This paper reports on experiments where a human operator, through a graphical user interface, watches robots perform an exploration task. The operator can manually suspend robots' movements before they crash into each other, and then resume their movements when their paths are clear. Experiment logs record the robots' states when they are paused and resumed. A behavior pattern for collision avoidance is learned, by classifying the states of the robots' environment when the human operator issues "wait" and "resume" commands. Preliminary results indicate that it is possible to learn a classifier which models these behavior patterns, and that different human operators consider different factors when making decisions about stopping and starting robots.

Intelligent systems promise to amplify, augment, and extend innate human abilities. A principal example is that of assistive rehabilitation robots---artificial intelligence and machine learning enable new electromechanical systems that restore biological functions lost through injury or illness. In order for an intelligent machine to assist a human user, it must be possible for a human to communicate their intentions and preferences to their non-human counterpart. While there are a number of techniques that a human can use to direct a machine learning system, most research to date has focused on the contrasting strategies of instruction and reward. The primary contribution of our work is to demonstrate that the middle ground between instruction and reward is a fertile space for research and immediate technological progress. To support this idea, we introduce the setting of human-prompted switching, and illustrate the successful combination of switching with interactive learning using a concrete real-world example: human control of a multi-joint robot arm. We believe techniques that fall between the domains of instruction and reward are complementary to existing approaches, and will open up new lines of rapid progress for interactive human training of machine learning systems.

List of organizers of the AAAI Fall Symposium on Robots Learning Interactively from Human Teachers.

There is strong theoretical evidence that aggregation of human judgments should not simply average multiple forecasts together (the unweighted linear opinion pool, or ULinOP), but weight them in such a way as to insure representation of a maximally diverse set of models among the experts from whom they are elicited. Explicitly eliciting these models places a major burden on the experts. We report on a variety of approaches to estimating these models, or at least the diversity among them, with minimal explicit input from the experts.

We are interested in aggregating forecasts of multinomial problems elicited from multiple experts. A common approach is to assign a weight to each expert, then form a weighted sum over their forecasts. Theoretical studies suggest that an important factor in such weighting is the diversity among experts. However, diversity is intrinsically a pairwise measure over experts, and does not lend itself naturally to a single weight that can be applied to an expert’s forecast in a weighted average. We suggest a way to take advantage of such pairwise measures in aggregating forecasts.

Many methods have been proposed for making use of multiple experts to predict uncertain events such as election outcomes, ranging from simple averaging of individual predictions to complex collaborative structures such as prediction markets or structured group decision making processes. We used a panel of more than 2,000 forecasters to systematically compare the performance of four different collaborative processes on a battery of political prediction problems. We found that teams and prediction markets systematically outperformed averages of individual forecasters, that training forecasters helps, and that the exact form of how predictions are combined has a large effect on overall prediction accuracy.

Twitter provides the freshest source of data about what is happening in the lives people across the world. The publicly available streams of status updates available on Twitter have been used to track earthquakes, forest fires and most especially flu outbreaks. Current techniques for tracking flu outbreaks rely on count data for a number of keywords. However, count data alone on the noisy Twitter streams is not reliable enough for health officials to make critical decisions. We propose a semi-automatic outbreak detection system. Rather than providing only alarms backed by count data, we propose a summarization system that will allow health officials to quickly verify outbreak alarms. This will lead to higher levels of trust in the system and allow the system to be used by health organizations around the world. We experimentally verify our summarization system and have found system users to have an accuracy of 0.86 when identifying multi-tweet summaries.