The Leiter International Performance Scale-Revised (Leiter-R) is a standardized cognitive test that seeks to "provide a nonverbal measure of general intelligence by sampling a wide variety of functions from memory to nonverbal reasoning." Understanding the computational building blocks of nonverbal cognition, as measured by the Leiter-R, is an important step towards understanding human nonverbal cognition, especially with respect to typical and atypical trajectories of child development. One subtest of the Leiter-R, Form Completion, involves synthesizing and localizing a visual figure from its constituent slices. Form Completion poses an interesting nonverbal problem that seems to combine several aspects of visual memory, mental rotation, and visual search. We describe a new computational cognitive model that addresses Form Completion using a novel, mental-rotation-friendly image representation that we call the Polar Augmented Resolution (PolAR) Picture, which enables high-fidelity mental rotation operations. We present preliminary results using actual Leiter-R test items and discuss directions for future work.

This paper presents a new Retinotopic Reasoning (R2) cognitive architecture that is inspired by studies of visual mental imagery in people. R2 is a hybrid symbolic-connectionist architecture, with certain components of the system represented in propositional, symbolic form, but with a primary working memory store that contains visual ``mental'' images that can be created and manipulated by the system. R2 is not intended to serve as a full-fledged, stand-alone cognitive architecture, but rather is a specialized system focusing on how visual mental imagery can be represented, learned, and used in support of intelligent behavior. Examples illustrate how R2 can be used to model human visuospatial cognition on several different standardized cognitive tests, including the Raven's Progressive Matrices test, the Block Design test, the Embedded Figures test, and the Paper Folding test.

Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence

The AAAI-17 workshop program included 17 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 4-5, 2017 at the Hilton San Francisco Union Square in San Francisco, California, USA. This report contains summaries of 12 of the workshops, and brief abstracts of the remaining 5

Most existing models of Stackelberg security games ignore the underlying topology of the space in which targets and defence resources are located. As a result, allocation of resources is restricted to a discrete collection of exogenously defined targets. However, in many practical security settings, defense resources can be located on a continuous plane. Better defense solutions could therefore be potentially achieved by placing resources in a space outside of actual targets (e.g., between targets). To address this limitation, we propose a model called Security Game on a Plane (SGP) in which targets are distributed on a 2-dimensional plane, and security resources, to be allocated on the same plane, protect targets within a certain effective distance. We investigate the algorithmic aspects of SGP. We find that computing a strong Stackelberg equilibrium of an SGP is NP-hard even for zero-sum games, and these are inapproximable in general. On the positive side, we find an exact solution technique for general SGPs based on an existing approach, and develop a PTAS (polynomial-time approximation scheme) for zero-sum SGP to more fundamentally overcome the computational obstacle. Our experiments demonstrate the value of considering SGP and effectiveness of our algorithms.

This paper and summary talk broadly survey computational sustainability research. Rather than a detailed treatment of the research projects in the area, which is beyond the scope of the paper and talk, the paper includes a meta-survey, pointing to edited collections and overviews in the literature for the interested reader. Computational sustainability research has been broadly characterized by AI methods employed, sustainability areas addressed, and contributions made to (typically, human) decision-making. The paper addresses these characterizations as well, which will facilitate a deeper synthesis later, to include the potential for developing sophisticated and holistic AI decision-making and advisory agents.

The quantity of information that is collected and stored in computer systems continues to grow rapidly. At the same time, the sensitivity of such information (e.g., detailed medical records) often makes such information valuable to both external attackers, who may obtain information by compromising a system, and malicious insiders, who may misuse information by exercising their authorization. To mitigate compromises and deter misuse, the security administrators of these resources often deploy various types of intrusion and misuse detection systems, which provide alerts of suspicious events that are worthy of follow-up review. However, in practice, these systems may generate a large number of false alerts, wasting the time of investigators. Given that security administrators have limited budget for investigating alerts, they must prioritize certain types of alerts over others. An important challenge in alert prioritization is that adversaries may take advantage of such behavior to evade detection — specifically by mounting attacks that trigger alerts that are less likely to be investigated. In this paper, we model alert prioritization with adaptive adversaries using a Stackelberg game and introduce an approach to compute the optimal prioritization of alert types. We evaluate our approach using both synthetic data and a real-world dataset of alerts generated from the audit logs of an electronic medical record system in use at a large academic medical center.

In this paper we discuss work performed in an ambitious DARPA funded cyber security effort. The broad approach taken by the project was for the network to be self-aware and to self-adapt in order to dodge attacks. In critical systems, it is not always the best or practical thing, to shut down the network under attack. The paper describes the qualitative trust modeling and diagnosis system that maintains a model of trust for networked resources using a combination of two basic ideas: Conditional trust (based on conditional preference (CP-Nets) and the principle of maximum entropy (PME)). We describe Monte-Carlo simulations of using adaptive security based on our trust model. The results of the simulations show the trade-off, under ideal conditions, between additional resource provisioning and attack mitigation.

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing.  The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them.  This, as an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself.  We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly. We use extensive experimentation to compare the methods with the traditional approach of inducing attention through rewarding the identification of the event of interest and within the three. The analysis of the results indicates that with the use of dummy events a substantially more favorable tradeoff between the detection (of the event of interest) probability and the expected expense can be achieved, and that among the three proposed method the one that decides on dummy events on the fly is (by far) the best.

User-generated content in social media is increasingly acknowledged as a rich resource for research into health problems. One particular area of interest is in the semantics individuals’ evoke because they can influence when health-related information is disclosed. While there have been multiple investigations into why self-disclose occurs, much less is known about when individuals choose to disclose information about other people (e.g., a relative), which is a significant privacy concern. In this paper, we introduce a novel framework to investigate how semantics influence disclosure routines for 34 health issues. This framework begins with a supervised classification model to distinguish tweets that communicate personal health issues from confounding concepts (e.g., metaphorical statements that include a health-related keyword). Next, we annotate tweets for each health issue with linguistic and psychological categories (e.g. social processes, affective processes and personal concerns). Then, we apply a non-negative matrix factorization over a health issue-by-language category space. Finally, the factorized basis space is leveraged to group health issues into natural aggregations based around how they are discussed. We evaluate this framework with four months of tweets (over 200 million) and show that certain semantics correspond with whom a health mention pertains to. Our findings show that health issues related with family members, high medical cost and social support (e.g., Alzheimer's Disease, cancer, and Down syndrome) lead to tweets that are more likely to disclose another individual's health status, while tweets with more benign health issues (e.g., allergy, arthritis, and bronchitis) with biological processes (e.g., health and ingestion) and negative emotions are more likely to contain self-disclosures.