A Computational Unification of Cognitive Control, Emotion, and Learning.

Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. In this thesis, we present a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in a theory of cognitive architecture (Soar). The theory of cognitive control specifies a set of required computational functions and their abstract inputs and outputs, while the appraisal theory specifies in more detail the nature of these inputs and outputs and an ontology for their representation. We argue that there is a surprising functional symbiosis between these two independently motivated theories that leads to a deeper theoretical integration than has been previously obtained in other computational treatments of cognition and emotion. We use an implemented model in Soar to test the feasibility of the resulting integrated theory, and explore its implications and predictive power in several task domains. With this integration, we then explore a possible functional benefit of emotion; namely, as an intrinsic motivator of reinforcement learning. This integration leads to other reinforcement learning extensions, such as automatic setting of the learning and exploration rate parameters.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60699/1/rmarinie_1.pd

Review of Intrinsic Motivation in Simulation-based Game Testing

This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored

Computational modeling of mood and feeling from emotion

We propose requirements for computational models that combine mood and emotion to create feeling and feeling intensity within an appraisal theory framework. Meeting these requirements involves solving many representational issues, such as determining the range of values for each appraisal dimension. We present functions that have been realized in a computational model that fulfill these requirements

Claims and Challenges in Evaluating Human-Level Intelligent Systems

This paper represents a first step in attempting to engage the research community in discussions about evaluation of human-level intelligent systems. First, we discuss the challenges of evaluating human-level intelligent systems. Second, we explore the different types of claims that are made about HLI systems, which are the basis for confirmatory evaluations. Finally, we briefly discuss a range of experimental designs that support the evaluation of claims

The molecular basis of lymphocyte recruitment to the skin: clues for pathogenesis and selective therapies of inflammatory disorders

Spatial compartmentalization and tissue-selective localization of T lymphocytes to the skin are crucial for immune surveillance and the pathogenesis of various disorders including common inflammatory diseases such as atopic dermatitis or psoriasis, but also malignancies such as cutaneous T cell lymphomas. Cutaneous recruitment of lymphocytes is a highly complex process that involves extravasation, migration through the dermal connective tissue, and eventually, localization to the epidermis. An intertwined network of cytokines and chemokines provides the road signs for leukocyte migration, while various adhesion receptors orchestrate the dynamic events of cell-cell and cell-substrate interactions resulting in cutaneous localization of T cells. Selectively targeting the functions of molecules involved in this interplay promises exciting new therapeutic options for treating inflammatory skin disorders