Using Neural Networks for Programming by Demonstration

Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies the desired emergent behavior of the system over time, and retrieves agent-level parameters required to execute that motion. A low time-complexity and data requirement favoring framework for reproducing emergent behavior, given an abstract demonstration, is discussed in [1], [2]. The existing framework does, however, observe an inherent limitation in scalability because of an exponentially growing search space (with the number of agent-level parameters). Our work addresses this limitation by pursuing a more scalable architecture with the use of neural networks. While the (proof-of-concept) architecture is not suitable for many evaluated domains because of its lack of representational capacity for that domain, it is more suitable than existing work for larger datasets for the Civil Violence agent-based model

Learning Non-Explicit Control Parameters of Self-Organizing Systems

Abstract—Controlling self-organizing systems with given control parameters is often unintuitive and inefficient for the user. Typically, there is some emergent behavior that the user would like to produce that is not directly controllable. Our goal is to develop an autonomous and domain-independent learning framework for adding non-explicit control parameters to selforganizing systems that provide users with more intuitive realtime control of the system. These additional controls are created by using regression to learn a mapping between the explicit control parameters and the non-explicit control parameters. I