Manufacturing intelligent agent simulation has not been
widely applied in industry because of its application
complexity. This complexity, which includes choosing
priority machines or jobs, determining machine maintenance
schedules, and allocating working shifts and breaks,
requires intelligent decision making. Manufacturing systems
are strongly influenced by intelligent decision makers.
Especially for...
This thesis details the applications of a new method of agent-based structure synthesis. The goal of this research is to create structures that meet a structural goal within a simulated physics environment. The method proposed works as an iterative process of changing the structure, evaluating the new structure and using...
Computing agents require state information to make coherent and useful decisions. A state representation is a numerical translation of the environment and conditions that are pertinent factors in an agent's decision making. Although many representations, when paired with clever learning algorithms, are able to coordinate and capture prey in specific...
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high
stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products...
Coordination in large multiagent systems in order to achieve a system level goal is a critical challenge. Given the agents' intention to cooperate, there is no guarantee that the agent actions will lead to good system objective especially when the system becomes large. One of the primary difficulties in such...
We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124...
Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each...