Matching Items (4)
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- All Subjects: automated planning
- Creators: Chiou, Erin
- Creators: Chakraborti, Tathagata
Description
Allocating tasks for a day's or week's schedule is known to be a challenging and difficult problem. The problem intensifies by many folds in multi-agent settings. A planner or group of planners who decide such kind of task association schedule must have a comprehensive perspective on (1) the entire array of tasks to be scheduled (2) idea on constraints like importance cum order of tasks and (3) the individual abilities of the operators. One example of such kind of scheduling is the crew scheduling done for astronauts who will spend time at International Space Station (ISS). The schedule for the crew of ISS is decided before the mission starts. Human planners take part in the decision-making process to determine the timing of activities for multiple days for multiple crew members at ISS. Given the unpredictability of individual assignments and limitations identified with the various operators, deciding upon a satisfactory timetable is a challenging task. The objective of the current work is to develop an automated decision assistant that would assist human planners in coming up with an acceptable task schedule for the crew. At the same time, the decision assistant will also ensure that human planners are always in the driver's seat throughout this process of decision-making.
The decision assistant will make use of automated planning technology to assist human planners. The guidelines of Naturalistic Decision Making (NDM) and the Human-In-The -Loop decision making were followed to make sure that the human is always in the driver's seat. The use cases considered are standard situations which come up during decision-making in crew-scheduling. The effectiveness of automated decision assistance was evaluated by setting it up for domain experts on a comparable domain of scheduling courses for master students. The results of the user study evaluating the effectiveness of automated decision support were subsequently published.
The decision assistant will make use of automated planning technology to assist human planners. The guidelines of Naturalistic Decision Making (NDM) and the Human-In-The -Loop decision making were followed to make sure that the human is always in the driver's seat. The use cases considered are standard situations which come up during decision-making in crew-scheduling. The effectiveness of automated decision assistance was evaluated by setting it up for domain experts on a comparable domain of scheduling courses for master students. The results of the user study evaluating the effectiveness of automated decision support were subsequently published.
ContributorsMIshra, Aditya Prasad (Author) / Kambhampati, Subbarao (Thesis advisor) / Chiou, Erin (Committee member) / Demakethepalli Venkateswara, Hemanth Kumar (Committee member) / Arizona State University (Publisher)
Created2019
Description
Previous literature was reviewed in an effort to further investigate the link between notification levels of a cell phone and their effects on driver distraction. Mind-wandering has been suggested as an explanation for distraction and has been previously operationalized with oculomotor movement. Mind-wandering’s definition is debated, but in this research it was defined as off task thoughts that occur due to the task not requiring full cognitive capacity. Drivers were asked to operate a driving simulator and follow audio turn by turn directions while experiencing each of three cell phone notification levels: Control (no texts), Airplane (texts with no notifications), and Ringer (audio notifications). Measures of Brake Reaction Time, Headway Variability, and Average Speed were used to operationalize driver distraction. Drivers experienced higher Brake Reaction Time and Headway Variability with a lower Average Speed in both experimental conditions when compared to the Control Condition. This is consistent with previous research in the field of implying a distracted state. Oculomotor movement was measured as the percent time the participant was looking at the road. There was no significant difference between the conditions in this measure. The results of this research indicate that not, while not interacting with a cell phone, no audio notification is required to induce a state of distraction. This phenomenon was unable to be linked to mind-wandering.
ContributorsRadina, Earl (Author) / Gray, Robert (Thesis advisor) / Chiou, Erin (Committee member) / Branaghan, Russell (Committee member) / Arizona State University (Publisher)
Created2019
Description
A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.
ContributorsChakraborti, Tathagata (Author) / Kambhampati, Subbarao (Thesis advisor) / Talamadupula, Kartik (Committee member) / Scheutz, Matthias (Committee member) / Ben Amor, Hani (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2018
Description
Decision support systems aid the human-in-the-loop by enhancing the quality of decisions and the ease of making them in complex decision-making scenarios. In the recent years, such systems have been empowered with automated techniques for sequential decision making or planning tasks to effectively assist and cooperate with the human-in-the-loop. This has received significant recognition in the planning as well as human computer interaction communities as such systems connect the key elements of automated planning in decision support to principles of naturalistic decision making in the HCI community. A decision support system, in addition to providing planning support, must be able to provide intuitive explanations based on specific user queries for proposed decisions to its end users. Using this as motivation, I consider scenarios where the user questions the system's suggestion by providing alternatives (referred to as foils). In response, I empower existing decision support technologies to engage in an interactive explanatory dialogue with the user and provide contrastive explanations based on user-specified foils to reach a consensus on proposed decisions. Furthermore, the foils specified by the user can be indicative of the latent preferences of the user. I use this interpretation to equip existing decision support technologies with three different interaction strategies that utilize the foil to provide revised plan suggestions. Finally, as part of my Master's thesis, I present RADAR-X, an extension of RADAR, a proactive decision support system, that showcases the above mentioned capabilities. Further, I present a user-study evaluation that emphasizes the need for contrastive explanations and a computational evaluation of the mentioned interaction strategies.
ContributorsValmeekam, Karthik (Author) / Kambhampati, Subbarao (Thesis advisor) / Chiou, Erin (Committee member) / Sengupta, Sailik (Committee member) / Arizona State University (Publisher)
Created2021