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Team decision making under stress involving multiple contexts is an extremely challenging issue faced by various real world application domains. This research is targeted at coupling cognitive agent technology and human-centered teamwork to address the informational challenges associated with Command and Control (C2) teams in contemporary military operations. Two sets of experiments, each with various settings of context switching frequencies and tasking complexities, were conducted. To ensure that the human subjects were familiar with the C2 context, they were selected from US Army ROTC (Reserve Officer Training Corps) students. Experiments on C2 teams that involve human subjects only were conducted first. We observed the decision making behavior of human subjects and incorporated expert behaviors into R-CAST-an agent architecture built upon a naturalistic decision making model that captures how domain experts make decisions based on experiences and situational similarity recognition. We then conducted another set of experiments with R-CAST agents as teammates and decision aids for human subjects. The results show that RPD-enabled agents can significantly improve the tasking capacity of C2 teams in multi-context decision making under stress. It also suggests that higher demand situations require more competent teammates.

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Current search engines do not support user searches for chemical entities (chemical names and formulae) beyond simple keyword searches. Usually a chemical molecule can be represented in multiple textual ways. A simple keyword search would retrieve only the exact match and not the others. We show how to build a search engine that enables searches for chemical entities and demonstrate empirically that it improves the relevance of returned documents. Our search engine first extracts chemical entities from text, performs novel indexing suitable for chemical names and formulae, and supports different query models that a scientist may require. We propose a model of hierarchical conditional random fields for chemical formula tagging that considers long-term dependencies at the sentence level. To substring searches of chemical names, a search engine must index substrings of chemical names. Indexing all possible sub-sequences is not feasible in practice. We propose an algorithm for independent frequent subsequence mining to discover sub-terms of chemical names with their probabilities. We then propose an unsupervised hierarchical text segmentation (HTS) method to represent a sequence with a tree structure based on discovered independent frequent subsequences, so that sub-terms on the HTS tree should be indexed. Query models with corresponding ranking functions are introduced for chemical name searches. Experiments show that our approaches to chemical entity tagging perform well. Furthermore, we show that index pruning can reduce the index size and query time without changing the returned ranked results significantly. Finally, experiments show that our approaches out-perform traditional methods for document search with ambiguous chemical terms.