This paper describes an investigation into the refinement of context -based human behavior models through the use of experiential learning. Specifically, a tactical agent was endowed with a context -based control model developed through other means and tasked with a mission in a simulation. This simulation-based mission was employed to expose the agent to situations possibly not considered in the model's original construction. Reinforcement learning was used to evaluate and refine the performance of this agent to improve its effectiveness and generality.
Machine learning is an algorithm or model that learns patterns in data and then predicts similar patterns in new data. For example, if you want to classify children's books, it would mean that instead of setting up precise rules for what constitutes a children's book, developers can feed the computer hundreds of examples of children's books. This predictive ability, in addition to the computer's ability to process massive amounts of data, enables ML to handle complex business situations with efficiency and accuracy. Traditionally, applications are programmed to make particular decisions, for example there may be a scenario based on predefined rules. These rules are based on human experience of the frequently-occurring scenarios.
Some define it as the global ability of an individual to think clearly and to function effectively in the environment; while others have much comparative definition like the ability to use self-knowledge in a good way to solve problems efficiently and quickly. However, despite a long history of research and debate, there is still no standard definition of intelligence. Seeing my blog topic you must have a thought of why I have started with intelligence. It is absolute necessary to overview intelligence before considering it for human or a machine. Artificial Intelligence is simulated intelligence in machines programmed to "think" like a human and mimic the way a person acts.
Niu, Yulei (Renmin University of China) | Lu, Zhiwu (Renmin University of China) | Huang, Songfang (IBM China Research Lab) | Gao, Xin (King Abdullah University of Science and Technology) | Wen, Ji-Rong (Renmin University of China)
We propose a novel approach, called FeaBoost, to image semantic segmentation with only image-level labels taken as weakly-supervised constraints. Our approach is motivated from two evidences: 1) each superpixel can be represented as a linear combination of basic components (e.g., predefined classes); 2) visually similar superpixels have high probability to share the same set of labels, i.e., they tend to have common combination of predefined classes. By taking these two evidences into consideration, semantic segmentation is formulated as joint feature and label refinement over superpixels. Furthermore, we develop an efficient FeaBoost algorithm to solve such optimization problem. Extensive experiments on the MSRC and LabelMe datasets demonstrate the superior performance of our FeaBoost approach in comparison with the state-of-the-art methods, especially when noisy labels are provided for semantic segmentation.
How do chatbots learn on their own and become "intelligent"? Read on to learn about the major approaches to developing self-learning chatbots. One of the questions we get asked by customers a lot is "Can your bot learn on its own?". The popular belief is that a bot is truly intelligent only when it's able to learn on its own. Here, we will examine what the aforementioned question really means, based on our experience building enterprise chatbots.