We analyze the patterns of autistic reasoning while performing planning tasks. The formalism of non-monotonic logic of defaults is used to simulate the autistic decision-making while adjusting an action to a context. Our current main finding is that while people with autism may be able to process single default rules, they have a characteristic difficulty in cases where multiple default rules conflict. Even though default reasoning was intended to simulate the reasoning of typical human subjects, it turns out that following the operational semantics of default reasoning in a literal way leads to the peculiarities of autistic behavior observed in the literature.

Traditionally, security decisions have been made without explicitly accounting for adaptive, intelligent attackers. Recent game theoretic security models have explicitly included attacker response in computing randomized security policies. Techniques to date, however, generally fail to explicitly account for interdependence between the targets to be secured, which is of vital importance in a variety of domains, including cyber, supply chain, and critical infrastructure security. We introduce a novel framework for computing optimal randomized security policies in networked domains which extends previous approaches in two ways. First, we extend previous linear programming techniques for Stackelberg security games to incorporate benefits and costs of arbitrary security configurations on individual assets. Second, we offer a principled model of failure cascades that allows us to capture both the direct and indirect value of assets. Finally, we use our framework to analyze four models, two based on random graph generation models, a simple model of interdependence between critical infrastructure and key resource sectors, and a model of the Fedwire interbank payment network.

In this paper we present a principled planner based approach to the active visual object search problem in unknown environments. We make use of a hierarchical planner that combines the strength of decision theory and heuristics. Furthermore, our object search approach leverages on the conceptual spatial knowledge in the form of object co-occurrences and semantic place categorisation. A hierarchical model for representing object locations is presented with which the planner is able to perform indirect search. Finally we present real world experiments to show the feasibility of the approach.

Stackelberg games have recently gained significant attention for resource allocation decisions in security settings. One critical assumption of traditional Stackelberg models is that all players are perfectly rational and that the followers perfectly observe the leader’s strategy. However, in real-world security settings, security agencies must deal with human adversaries who may not always follow the utility maximizing rational strategy. Accounting for these likely deviations is important since they may adversely affect the leader’s (security agency’s) utility. In fact, a number of behavioral gametheoretic models have begun to emerge for these domains. Two such models in particular are COBRA (Combined Observability and Bounded Rationality Assumption) and BRQR (Best Response to Quantal Response), which have both been shown to outperform game-theoretic optimal models against human adversaries within a security setting based on Los Angeles International Airport (LAX). Under perfect observation conditions, BRQR has been shown to be the leading contender for addressing human adversaries. In this work we explore these models under limited observation conditions. Due to human anchoring biases, BRQR’s performance may suffer under limited observation conditions. An anchoring bias is when, given no information about the occurrence of a discrete set of events, humans will tend to assign an equal weight to the occurrence of each event (a uniform distribution). This study makes three main contributions: (i) we incorporate an anchoring bias into BRQR to improve performance under limited observation; (ii) we explore finding appropriate parameter settings for BRQR under limited observation; (iii) we compare BRQR’s performance versus COBRA under limited observation conditions.

In this paper we describe the integration of a robot control platform (Player/Stage) and a real robot (Pioneer P3DX) with PELEA (Planning, Execution and LEarning Architecture). PELEA is a general-purpose planning architecture suitable for a wide range of real world applications, from robotics to emergency management. It allows planning engineers to generate planning applications since it integrates planning, execution, replanning, monitoring and learning capabilities. We also present a relational learning approach for automatically modeling robot-action execution durations, with the purpose of improving the planning process of PELEA by refining domain definitions.

Effective visualization resizing is important for many visualization tasks, where users may have display devices with different sizes and aspect ratios. Our recently designed framework can adapt a visualization to different displays by transforming the resizing problem into a non-linear optimization problem. However, it is not scalable to a large amount of dense information. Undesired cluttered results would be produced if dense information is presented in the target display. We present an extension to our resizing framework with a seamless integration of a sampling-based data abstraction mechanism, such that it is scalable with not only different display sizes, but also different amounts of information.

Using these custom tools requires have been busily converting material from paper and microfilm training and a skilled workforce. We show in this paper that into digital domain. Newspapers, books, journals and some parts of that process can be distributed to a pool of even individual letters are finding themselves inside large unskilled volunteers with good results.

We present current work on the NextSlidePlease slide- ware presentation tool and discuss how mixed-initiative principles may support the complex tasks of combin- ing multiple linear presentations into a network of re- lated content. We discuss future directions in two areas: supporting the layout of complex sets of interconnected slides, and refining the time requirements and content importance in these networks.

The creation of high fidelity scenarios for disaster simulation is a major challenge for a number of reasons. First, the maps supplied by existing map providers tend to provide only road or building shapes and do not accurately model open spaces which people use to evacuate buildings, homes, or industrial facilities. Secondly, even if some of the data about evacuation routes is available, the real-world connection points between these spaces and roads and buildings is usually not well defined unless data from buildings’ owners can be obtained. Finally, in order to augment current maps with accurate spatial data, it would require either a good set of training data for a computer vision algorithm to define evacuation routes using pictures or a significant amount of manpower to directly survey a vast area. Against this background, we develop a novel model of geospatial data creation, called CollabMap, that relies on human computation. CollabMap is a crowdsourcing tool to get users contracted via Amazon Mechanical Turk or a similar service to perform micro-tasks that involve augmenting existing maps by drawing evacuation routes, using satellite imagery from Google Maps and panoramic views from Google Street-View. We use human computation to complete tasks that are hard for a computer vision algorithm to perform or to generate training data that could be used by a computer vision algorithm to automatically define evacuation routes.

In this paper, we develop a new human computation algorithm for speech-to-text transcription that can potentially achieve the high accuracy of professional transcription using only microtasks deployed via an online task market or a game. The algorithm partitions audio clips into short 10-second segments for independent processing and joins adjacent outputs to produce the full transcription. Each segment is sent through an iterative dual pathway structure that allows participants in either path to iteratively refine the transcriptions of others in their path while being rewarded based on transcriptions in the other path, eliminating the need to check transcripts in a separate process. Initial experiments with local subjects show that produced transcripts are on average 96.6% accurate.