Emergence of Agency from a Causal Perspective

dc.contributor.authorÅnestrand, Alvin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerLundh, Torbjörn
dc.contributor.supervisorHäggström, Olle
dc.contributor.supervisorFox, James
dc.contributor.supervisorEveritt, Tom
dc.date.accessioned2024-08-13T10:40:32Z
dc.date.available2024-08-13T10:40:32Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractCausal models of agents and agentic behavior allows for safety analysis of machine learning systems. Understanding how goal-directed behavior emerges from adapting to an environment is however non-trivial. This thesis addresses the gap between theoretical models and real-world implementations of machine learning systems, though a framework that formalizes the connection between system dynamics and goal-driven behavior. This thesis introduces novel probabilistic graphical models for describing system dynamics involving learning agents, based on dynamic bayesian networks, which allows for a flexible representation of causal relationships in the training environment. To analyze goal-directed behavior that emerges from interacions between agents and the environment, the thesis also introduces temporally abstracted models. Such a model captures the dynamics of a system after the learning process has converged, derived from a model of the learning process. A temporally abstracted model describes potential outcomes involving equilibria between agents and the environment, and can under certain conditions be viewed as a model of goal-directed behavior in the system.
dc.identifier.coursecodeMVEX03
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308368
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectIntelligent agent, objective, causal influence diagram, causal model, emergence, learning, equilibria, dynamical systems, goal-directed behavior
dc.titleEmergence of Agency from a Causal Perspective
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeData science and AI (MPDSC), MSc
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