This work investigates the origins of behavioral diversity in learning robot teams. Behavioral diversity refers to the extent to which agents assume distinct behavioral roles in a group. Most research in multi-robot teams ...

This paper describes research investigating behavioral specialization in learning robot teams. Each agent is provided a common set of skills (motor schema-based behavioral assemblages) from which it builds a task-achie ...

Computational creativity is a field focused on the study and development of behaviors in computers an observer would deem creative. Traditionally, it has relied upon rules-based and search-based artificial intelligence. ...

With continued advances in science and technology, big graph (or network) data, such as World Wide Web, social networks, academic collaboration networks, transportation networks, telecommunication networks, biological ...

Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming manual task. Current state-of-the-art ...

Continuous-Time Hidden Markov Model (CT-HMM) is a useful tool for modeling disease progression from noisy observed data arriving irregularly in time. However, the lack of any widely-accepted efficient learning algorithm ...

Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to maximize goal attainment. However, reinforcement learning algorithms ...

Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and iteratively altering a game design. ...

While Artificial Intelligence (AI) has tremendous potential as a defense against real-world cybersecurity threats, understanding the capabilities and robustness of AI remains a fundamental challenge. This dissertation ...