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Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Neural Information Processing Systems

Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain. This model bottlenecks the view of an agent by a soft, top-down attention mechanism, forcing the agent to focus on task-relevant information by sequentially querying its view of the environment. The output of the attention mechanism allows direct observation of the information used by the agent to select its actions, enabling easier interpretation of this model than of traditional models. We analyze the different strategies the agents learn and show that a handful of strategies arise repeatedly across different games. We also show that the model learns to query separately about space and content ( where'' vs. what'').


Learning by Observation of Agent Software Images

Journal of Artificial Intelligence Research

Learning by observation can be of key importance whenever agents sharing similar features want to learn from each other. This paper presents an agent architecture that enables software agents to learn by direct observation of the actions executed by expert agents while they are performing a task. This is possible because the proposed architecture displays information that is essential for observation, making it possible for software agents to observe each other. The agent architecture supports a learning process that covers all aspects of learning by observation, such as discovering and observing experts, learning from the observed data, applying the acquired knowledge and evaluating the agent's progress. The evaluation provides control over the decision to obtain new knowledge or apply the acquired knowledge to new problems.


A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction

arXiv.org Artificial Intelligence

In this paper, we propose an AI-FML robotic agent for student learning behavior ontology construction which can be applied in English speaking and listening domain. The AI-FML robotic agent with the ontology contains the perception intelligence, computational intelligence, and cognition intelligence for analyzing student learning behavior. In addition, there are three intelligent agents, including a perception agent, a computational agent, and a cognition agent in the AI-FML robotic agent. We deploy the perception agent and the cognition agent on the robot Kebbi Air. Moreover, the computational agent with the Deep Neural Network (DNN) model is performed in the cloud and can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent is applied in Taiwan and tested in Japan. The experimental results show that the agents can be utilized in the human and machine co-learning model for the future education.


Clever Artificial Intelligence hides information to cheat later at task - details inside Tech News

#artificialintelligence

California: Artificial Intelligence has become so intelligent that it is learning when to hide a some information which can be used later. A research from Stanford and Google discovered that a machine learning agent tasked with transforming aerial images into map was hiding information in order to cheat later. CycleGAN is a neural network that learns to transform images. In the early results, the machine learning agent was doing well but later when it was asked to do the reverse process of reconstructing aerial photographs from street maps it showed up information which was eliminated in the first process, TechCrunch reported. For instance, skylights on a roof that were eliminated in the process of creating a street map would reappear when the agent was asked to reverse the process.


Clever Artificial Intelligence Hides Information to Cheat Later at Task

#artificialintelligence

Artificial Intelligence has become so intelligent that it is learning when to hide some information which can be used later. Research from Stanford University and Google discovered that a machine learning agent tasked with transforming aerial images into map was hiding information in order to cheat later. CycleGAN is a neural network that learns to transform images. In the early results, the machine learning agent was doing well but later when it was asked to do the reverse process of reconstructing aerial photographs from street maps it showed up information which was eliminated in the first process, TechCrunch reported. For instance, skylights on a roof that were eliminated in the process of creating a street map would reappear when the agent was asked to reverse the process.