Gabriel Ganberg

Bio: Gabriel Ganberg is an academic researcher. The author has contributed to research in topics: Context (language use) & Decision support system. The author has an hindex of 4, co-authored 8 publications receiving 30 citations.

A General Context-Aware Framework for Improved Human-System Interactions

Abstract: For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.

Inferring user intent with Bayesian inverse planning: Making sense of multi-UAS mission management

Abstract: The goal of intent inference is to use observed behavior to predict underlying mental states and causal processes that are likely to have generated the behavior. A potentially powerful technique for inferring intent uses Bayesian inference in structured generative models for planning. We describe our adaptation of the Bayesian inverse planning framework to the multi-unmanned systems mission planning domain. We describe three experiments that elucidate the space of planning priorities in the domain, infer users’ goals and priorities given their actions with a planning user interface, and predict users’ next planning actions using inferences about their goals and priorities.

Representing context using the context for human and automation teams model

Abstract: The goal of representing context in a mixed initiative system is to model the information at a level of abstraction that is actionable for both the human and automated system. A potential solution to this problem is the Context for Human and Automation Teams (CHAT). This paper introduces the CHAT model and provides example implementations from several different applications such as task scheduling techniques, multi-agent systems, and human-robot interaction.

A Testbed for Investigating Task Allocation Strategies between Air Traffic Controllers and Automated Agents

Abstract: To meet the growing demands of the National Airspace System (NAS) stakeholders and provide the level of service, safety and security needed to sustain future air transport, the Next Generation Air Transportation System (NextGen) concept calls for technologies and systems offering increasing support from automated systems that provide decision-aiding and optimization capabilities. This is an exciting application for some core aspects of Artificial Intelligence research since the automation must be designed to enable the human operators to access and process a myriad of information sources, understand heightened system complexity, and maximize capacity, throughput and fuel savings in the NAS.. This paper introduces an emerging application of techniques from mixed initiative (adjustable autonomy), multi-agent systems, and task scheduling techniques to the air traffic control domain. Consequently, we have created a testbed for investigating the critical challenges in supporting the early design of systems that allow for optimal, context-sensitive function (role) allocation between air traffic controller and automated agents. A pilot study has been conducted with the testbed and preliminary results show a marked qualitative improvement in using dynamic function allocation optimization versus static function allocation.

The Evolution of User Experiences and Interfaces for Delivering Context-Aware Recommendations to Information Analysts

Abstract: Rigor in the products of information analysis is essential for decision makers to rely on the assessments contained within them. Zelik, Patterson, and Woods [1] defined an eight-attribute metric for communicating the rigor of analytic products. This paper describes two iterations of the process of designing, implementing, and evaluating a context-aware web application that uses this analytic rigor metric to recommend augmentations to analysts’ workflow that will improve the quality of the resultant products. We used multiple methods to evaluate this tool with subject matter experts, including brainstorming, collaborative card sorting, semi-structured interviews, cognitive walkthroughs, and heuristic evaluations. This research found that: (1) it is critical to have flexible recommendations that adapt to movements between foraging and sense-making components of workflow, and the changing structure of the analysis; and (2) persistent visualizations of analytic rigor assessments are distracting, and promote interpretation as a performance metric rather than a process aid.

Effects of adaptive task allocation on monitoring of automated systems

Abstract: The effects of adaptive task allocation on monitoring for automation failure during multitask flight simulation were examined. Participants monitored an automated engine status task while simultaneously performing tracking and fuel management tasks over three 30-min sessions. Two methods of adaptive task allocation, both involving temporary return of the automated engine status task to the human operator ("human control"), were examined as a possible countermeasure to monitoring inefficiency. For the model-based adaptive group, the engine status task was allocated to all participants in the middle of the second session for 10 min, following which it was again returned to automation control. The same occurred for the performance-based adaptive group, but only if an individual participant's monitoring performance up to that point did not meet a specified criterion. For the nonadaptive control groups, the engine status task remained automated throughout the experiment. All groups had low probabilities of detection of automation failures for the first 40 min spent with automation. However, following the 10-min intervening period of human control, both adaptive groups detected significantly more automation failures during the subsequent blocks under automation control. The results show that adaptive task allocation can enhance monitoring of automated systems. Both model-based and performance-based allocation improved monitoring of automation. Implications for the design of automated systems are discussed.

Artificial Intelligence for Social Good: A Survey

Abstract: Artificial intelligence for social good (AI4SG) is a research theme that aims to use and advance artificial intelligence to address societal issues and improve the well-being of the world. AI4SG has received lots of attention from the research community in the past decade with several successful applications. Building on the most comprehensive collection of the AI4SG literature to date with over 1000 contributed papers, we provide a detailed account and analysis of the work under the theme in the following ways. (1) We quantitatively analyze the distribution and trend of the AI4SG literature in terms of application domains and AI techniques used. (2) We propose three conceptual methods to systematically group the existing literature and analyze the eight AI4SG application domains in a unified framework. (3) We distill five research topics that represent the common challenges in AI4SG across various application domains. (4) We discuss five issues that, we hope, can shed light on the future development of the AI4SG research.

Railway-controller-perceived competence in incidents and accidents.

Abstract: Railway controllers play a pivotal role in service recovery of normal rail system operations when incidents and accidents occur. Those in this position must have sufficient competence to overcome task difficulties caused by accident uncertainties. This study adopts Taiwan's railway system as a case study to diagnose railway-controller-perceived competence when facing diverse tasks during incidents and accidents that are derived from a proposed conceptual model. Railway-controller-perceived competence is measured using the Rasch model. Analytical results indicate that working with an external rescue agency handling a rescue operation, explanations to the public, and communication with an external rescue agency are considered the most troublesome tasks. Additionally, railway-controller-perceived competence differs based on the work experience. This information will prove useful for rail system operators and government regulators when designing and regulating railway controller competence management systems....

A Decentralized Partially Observable Decision Model for Recognizing the Multiagent Goal in Simulation Systems

Abstract: Multiagent goal recognition is important in many simulation systems. Many of the existing modeling methods need detailed domain knowledge of agents’ cooperative behaviors and a training dataset to estimate policies. To solve these problems, we propose a novel decentralized partially observable decision model (Dec-POMDM), which models cooperative behaviors by joint policies. In this compact way, we only focus on the distribution of joint policies. Additionally, a model-free algorithm, cooperative colearning based on Sarsa, is exploited to estimate agents’ policies under the assumption of rationality, which makes the training dataset unnecessary. In the inference, considering that the Dec-POMDM is discrete and its state space is large, we implement a marginal filter (MF) under the framework of the Dec-POMDM, where the initial world states and results of actions are uncertain. In the experiments, a new scenario is designed based on the standard predator-prey problem: we increase the number of preys, and our aim is to recognize the real target of predators. Experiment results show that (a) our method recognizes goals well even when they change dynamically; (b) the Dec-POMDM outperforms supervised trained HMMs in terms of precision, recall, and F-measure; and (c) the MF infers goals more efficiently than the particle filter under the framework of the Dec-POMDM.