186 research outputs found
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
We pose an active perception problem where an autonomous agent actively
interacts with a second agent with potentially adversarial behaviors. Given the
uncertainty in the intent of the other agent, the objective is to collect
further evidence to help discriminate potential threats. The main technical
challenges are the partial observability of the agent intent, the adversary
modeling, and the corresponding uncertainty modeling. Note that an adversary
agent may act to mislead the autonomous agent by using a deceptive strategy
that is learned from past experiences. We propose an approach that combines
belief space planning, generative adversary modeling, and maximum entropy
reinforcement learning to obtain a stochastic belief space policy. By
accounting for various adversarial behaviors in the simulation framework and
minimizing the predictability of the autonomous agent's action, the resulting
policy is more robust to unmodeled adversarial strategies. This improved
robustness is empirically shown against an adversary that adapts to and
exploits the autonomous agent's policy when compared with a standard
Chance-Constraint Partially Observable Markov Decision Process robust approach
Software agents in music and sound art research/creative work: Current state and a possible direction
Composers, musicians and computer scientists have begun to use software-based agents to create music and sound art in both linear and non-linear (non-predetermined form and/or content) idioms, with some robust approaches now drawing on various disciplines. This paper surveys recent work: agent technology is first introduced, a theoretical framework for its use in creating music/sound art works put forward, and an overview of common approaches then given. Identifying areas of neglect in recent research, a possible direction for further work is then briefly explored. Finally, a vision for a new hybrid model that integrates non-linear, generative, conversational and affective perspectives on interactivity is proposed
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