Designing user-interfaces so that first-time or infrequent users can accomplish their goals by exploration has been an enduring challenge in Human-Computer Interaction. Iterative user-testing is an effective but costly method to develop user-interfaces that support use through exploration. A complementary method is to use modeling tools that can generate predictions of user exploration given a user-interface and a goal description. Recent computational models of goal-directed user exploration have focused on predicting user exploration of websites and demonstrated how predictions can inform user-interface design. These models employ the common concepts of label following and information scent: that the user's choice is partly determined by the semantic relevance between the user's goal and the options presented in the user-interface. However, in addition to information scent, other factors including the layout position and grouping of options in the user-interface also affect user exploration and the likelihood of success. This dissertation contributes a new model of goal-directed user exploration, called CogTool- Explorer, which considers the layout position and the grouping of options in the user-interface in concert with a serial evaluation visual search process and information scent. Tests show that predictions from CogTool-Explorer match participant data better than alternative models that do not consider layout position and grouping. This dissertation work has also integrated the CogTool- Explorer model into an existing modeling tool, called CogTool, making it easier for other researchers and practitioners to setup and generate predictions of likely user exploration paths and task performance using CogTool-Explorer.

Context-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a context sensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs use a continuous context-based representation for a given problem-solving scenario for decision-support processes. Both formalisms use contexts dynamically by continuously changing between necessary contexts as needed in appropriate instances. This thesis identifies a synergy between these two formalisms by looking into their similarities and differences. It became clear during the research that each paradigm was designed with a very specific family of problems in mind. Thus, CXBR best implements models of autonomous agents in environment, while CxGs is best implemented in a decision support setting that requires the development of decision-making procedures. Cross applications were implemented on each and the results are discussed.