Emotional Experience, Paranoia, and Probabilistic Reasoning in Schizophrenia

Schizophrenia (SZ) is a chronic mental disorder characterized by longstanding and severe social functioning deficits. In trying to better understand psychosocial factors that perpetuate these functional deficits, this dissertation included three studies that examine cognitive and affective factors with the potential to improve functional outcomes in SZ: 1) emotional experience, 2) paranoia, and 3) reasoning. Study one examined negative/positive affect and social functioning with self-report measures among SZ, affective disorders, and the general population. Study 2 assessed paranoia and its relationship with the interpretation of the environment via affective sound localization in SZ. Study 3 compared probabilistic reasoning when estimating the likely source of threatening and non-threatening affective stimuli while also examining the relationship between probabilistic reasoning and delusional thinking in SZ. The findings of this dissertation suggest that for people with schizophrenia: 1) treatment of heightened negative affect and reduced positive affect may improve social functioning, 2) paranoia may aid localization of natural sounds that occur in the environment, and 3) promoting more conservative probabilistic reasoning may help to reduce delusional thinking.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146039/1/tylerg_1.pd

Equilibria-based Probabilistic Model Checking for Concurrent Stochastic Games

Probabilistic model checking for stochastic games enables formal verification of systems that comprise competing or collaborating entities operating in a stochastic environment. Despite good progress in the area, existing approaches focus on zero-sum goals and cannot reason about scenarios where entities are endowed with different objectives. In this paper, we propose probabilistic model checking techniques for concurrent stochastic games based on Nash equilibria. We extend the temporal logic rPATL (probabilistic alternating-time temporal logic with rewards) to allow reasoning about players with distinct quantitative goals, which capture either the probability of an event occurring or a reward measure. We present algorithms to synthesise strategies that are subgame perfect social welfare optimal Nash equilibria, i.e., where there is no incentive for any players to unilaterally change their strategy in any state of the game, whilst the combined probabilities or rewards are maximised. We implement our techniques in the PRISM-games tool and apply them to several case studies, including network protocols and robot navigation, showing the benefits compared to existing approaches

Expert system development for probabilistic load simulation

A knowledge based system LDEXPT using the intelligent data base paradigm was developed for the Composite Load Spectra (CLS) project to simulate the probabilistic loads of a space propulsion system. The knowledge base approach provides a systematic framework of organizing the load information and facilitates the coupling of the numerical processing and symbolic (information) processing. It provides an incremental development environment for building generic probabilistic load models and book keeping the associated load information. A large volume of load data is stored in the data base and can be retrieved and updated by a built-in data base management system. The data base system standardizes the data storage and retrieval procedures. It helps maintain data integrity and avoid data redundancy. The intelligent data base paradigm provides ways to build expert system rules for shallow and deep reasoning and thus provides expert knowledge to help users to obtain the required probabilistic load spectra

Real-time value-driven diagnosis

Diagnosis is often thought of as an isolated task in theoretical reasoning (reasoning with the goal of updating our beliefs about the world). We present a decision-theoretic interpretation of diagnosis as a task in practical reasoning (reasoning with the goal of acting in the world), and sketch components of our approach to this task. These components include an abstract problem description, a decision-theoretic model of the basic task, a set of inference methods suitable for evaluating the decision representation in real-time, and a control architecture to provide the needed continuing coordination between the agent and its environment. A principal contribution of this work is the representation and inference methods we have developed, which extend previously available probabilistic inference methods and narrow, somewhat, the gap between probabilistic and logical models of diagnosis