Experts and novices differ with respect to the use of intuition and deliberation in their decision-making processes, which affects the quality of decisions they make. We often ask ourselves: where does expert intuition and deliberation come from? If the progression from novice to expert is made through learning experiences, what should we provide novices during training? Identifying the discrepancies between experts and novices is essential for developing a computational learning system that simulates the human decision-making process. In this paper, we investigate the difference between individuals operating Unmanned Aerial Vehicle (UAV) missions, collected in a dataset called the Supervisory Control Operations User Test bed (SCOUT), by analyzing their computational models. For the computational models, deep neural networks (DNNs) and double transition models (DTMs) were employed. A set of DNNs was constructed from biometric information about eye movements, and a set of DTMs was built from event-driven data associated with actions taken by the individuals. For investigating DNNs, we examined how much improvement was obtained during training and validation, while for DTMs, we concentrated on the reward distributions of trajectories derived through inverse reinforcement learning (IRL). We classified SCOUT subjects into three levels of expertise according to their selfassessment and the maximum score achieved: novice, intermediate and expert. By analyzing these models, we identified differences between the expert and novice groups. In particular, the accuracy of the expert DNNs improved more effectively than that of novice DNNs, and the reward distributions of the expert DTMs were more closely clustered than those of novice DTMs.
A Commander’s decision making style represents how he weighs his choices and evaluates possible solutions with regards to his goals. Specifically, in the naval warfare domain, it relates the way he processes a large amount of information in dynamic, uncertain environments, allocates resources, and chooses appropriate actions to pursue. In this paper, we describe an approach to capture a Commander’s decision style by creating a cognitive model that captures his decisionmaking process and evaluate this model using a set of scenarios using an online naval warfare simulation game. In this model, we use the Commander’s past behaviors and generalize Commander's actions across multiple problems and multiple decision making sequences in order to recommend actions to a Commander in a manner that he may have taken. Our approach builds upon the Double Transition Model to represent the Commander's focus and beliefs to estimate his cognitive state. Each cognitive state reflects a stage in a Commander’s decision making process, each action reflects the tasks that he has taken to move himself closer to a final decision, and the reward reflects how close he is to achieving his goal. We then use inverse reinforcement learning to compute a reward for each of the Commander's actions. These rewards and cognitive states are used to compare between different styles of decision making. We construct a set of scenarios in the game where rational, intuitive and spontaneous decision making styles will be evaluated.
Intelligent Foraging, Gathering and Matching (I-FGM) combines a unique multi-agent architecture with a novel partial
processing paradigm to provide a solution for real-time information retrieval in large and dynamic databases. I-FGM
provides a unified framework for combining the results from various heterogeneous databases and seeks to provide
easily verifiable performance guarantees. In our previous work, I-FGM had been implemented and validated with
experiments on dynamic text data. However, the heterogeneity of search spaces requires our system having the ability to
effectively handle various types of data. Besides texts, images are the most significant and fundamental data for
information retrieval. In this paper, we extend the I-FGM system to incorporate images in its search spaces using a
region-based Wavelet Image Retrieval algorithm called WALRUS. Similar to what we did for text retrieval, we
modified the WALRUS algorithm to partially and incrementally extract the regions from an image and measure the
similarity value of this image. Based on the obtained partial results, we refine our computational resources by updating
the priority values of image documents. Experiments have been conducted on I-FGM system with image retrieval. The
results show that I-FGM outperforms its control systems. Also, in this paper we present theoretical analysis of the
systems with a focus on performance. Based on probability theory, we provide models and predictions of the average
performance of the I-FGM system and its two control systems, as well as the systems without partial processing.
To quickly find relevant information from huge amounts of data is a very challenging issue for intelligence analysts.
Most employ their prior domain knowledge to improve their process of finding relevant information. In this paper, we
explore the influences of a user's prior domain knowledge on the effectiveness of an information seeking task by using
seed user models in an enhanced information retrieval system. In our approach, a user model is created to capture a
user's intent in an information seeking task. The captured user intent is then integrated with the attributes describing an
information retrieval system in a decision theoretic framework. Our test bed consists of two benchmark collections from
the information retrieval community: MEDLINE and CACM. We divide each query set from a collection into two
subsets: training set and testing set. We use three different approaches to selecting the queries for the training set: (1) the
queries generating large domain knowledge, (2) the queries relating to many other queries, and (3) a mixture of (1) and
(2). Each seed user model is created by running our enhanced information retrieval system through such a training set.
We assess the effects of having more domain knowledge, or more relevant domain knowledge, or a mixture of both on
the effectiveness of a user in an information seeking task.
Intelligent foraging, gathering and matching (I-FGM) has been shown to be an effective tool for intelligence analysts
who have to deal with large and dynamic search spaces. I-FGM introduced a unique resource allocation strategy based
on a partial information processing paradigm which, along with a modular system architecture, makes it a truly novel
and comprehensive solution to information retrieval in such search spaces. This paper provides further validation of its
performance by studying its behavior while working with highly dynamic databases. Results from earlier experiments
were analyzed and important changes have been made in the system parameters to deal with dynamism in the search
space. These changes also help in our goal of providing relevant search results quickly and with minimum wastage of
computational resources. Experiments have been conducted on I-FGM in a realistic and dynamic simulation
environment, and its results are compared with two other control systems. I-FGM clearly outperforms the control
systems.
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