A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. © 2022, The Author(s)

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

Adaptive Agent-Based Simulation for Individualized Training

Agent-based simulation can be used for efficient and effective training of human operators and decision-makers. However, constructing realistic behavior models for the agents is challenging and time-consuming, especially for subject matter experts, who may not have expertise in artificial intelligence. In this work, we investigate how machine learning can be used to adapt simulation contents to the current needs of individual trainees. Our initial results demonstrate that multi-objective multi-agent reinforcement learning is a promising approach for creating agents with diverse and adaptive characteristics, which can stimulate humans in training

Byggnads- och träd- parametrisering i halvt skymda 2.5D digitala höjdmodeller

Automatic 3D building reconstruction has been a hot research area; a task which has been done manually even up today. Automating the task of building reconstruction enables more applications where up to date information is of great importance. This thesis proposes a system to extract parametric buildings and trees from dense aerial stereo image data. The method developed for the tree identification and parameterization is a totally new approach which have yielded great results. The focus has been to extract the data in such a way that small flying platforms can use it for navigational purposes. The degree of simplification is therefor high. The building parameterization part starts with identifying roof faces by Region Growing random seeds in the digital surface model (DSM) until a coverage threshold is met.For each roof face a plane is fitted using a Least Square approach.The actual parameterization is started with calculating the intersection between the roof faces. Given the nature of 2.5D DSM data there is no possibility to perform wall fitting. Therefor all the walls will be constructed with a 2D line Hough transform of the border data of all the roof faces. The tree parameterization is done by searching for possible roof face topologies resembling the signature of a tree. For each possible tree topology a second degree polynomial surface is fitted to the DSM data covered by the faces in the topology. By looking at the parameters of the fitted polynomial it is then possible to determine if it is a tree or not. All the extraction steps were implemented and evaluated in Matlab, all algorithms have been described, discussed and  motivated in the thesis

Adaptive Agent-Based Simulation for Individualized Training

Agent-based simulation can be used for efficient and effective training of human operators and decision-makers. However, constructing realistic behavior models for the agents is challenging and time-consuming, especially for subject matter experts, who may not have expertise in artificial intelligence. In this work, we investigate how machine learning can be used to adapt simulation contents to the current needs of individual trainees. Our initial results demonstrate that multi-objective multi-agent reinforcement learning is a promising approach for creating agents with diverse and adaptive characteristics, which can stimulate humans in training

Byggnads- och träd- parametrisering i halvt skymda 2.5D digitala höjdmodeller

Automatic 3D building reconstruction has been a hot research area; a task which has been done manually even up today. Automating the task of building reconstruction enables more applications where up to date information is of great importance. This thesis proposes a system to extract parametric buildings and trees from dense aerial stereo image data. The method developed for the tree identification and parameterization is a totally new approach which have yielded great results. The focus has been to extract the data in such a way that small flying platforms can use it for navigational purposes. The degree of simplification is therefor high. The building parameterization part starts with identifying roof faces by Region Growing random seeds in the digital surface model (DSM) until a coverage threshold is met.For each roof face a plane is fitted using a Least Square approach.The actual parameterization is started with calculating the intersection between the roof faces. Given the nature of 2.5D DSM data there is no possibility to perform wall fitting. Therefor all the walls will be constructed with a 2D line Hough transform of the border data of all the roof faces. The tree parameterization is done by searching for possible roof face topologies resembling the signature of a tree. For each possible tree topology a second degree polynomial surface is fitted to the DSM data covered by the faces in the topology. By looking at the parameters of the fitted polynomial it is then possible to determine if it is a tree or not. All the extraction steps were implemented and evaluated in Matlab, all algorithms have been described, discussed and  motivated in the thesis