When intelligent agents act in a stochastic environment, the principle of maximizing expected rewards is used to optimize their policies. The rationality of the maximum rewards becomes a single objective when agents’...
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When intelligent agents act in a stochastic environment, the principle of maximizing expected rewards is used to optimize their policies. The rationality of the maximum rewards becomes a single objective when agents’ decision problems are solved in most cases. This sometimes leads to the agents’ behaviors (the optimal policies for solving the decision problems) that are not legible. In other words, it is difficult for users (or other agents and even humans) to understand the agents’ intentions when they are executing the optimal policies. Hence, it becomes pertinent to consider the legibility of agents’ decision problems. The key challenge lies in formulating a proper legibility function in the problems. Using domain experts’ inputs leans to be subjective and inconsistent in specifying legibility values, and the manual approach quickly becomes infeasible in a complex problem domain. In this article, we aim to learn such a legibility function parallel to developing a (conventional) reward function. We adopt inverse reinforcement learning techniques to automate a legibility function in agents’ decision problems. We first demonstrate the effectiveness of the inverse reinforcement learning technique when legibility is solely considered in a decision problem. Things become complicated when both the reward and legibility functions are to be found. We develop a multi-objective inverse reinforcement learning method to automate the two functions in a good balance simultaneously. We vary problem domains in the performance study and provide empirical results in support.
After two decades, data processing has finally, and probably forever, found its niche among civil engineering and construction (CEC) professionnals, through word processors, digitizing tables, management software, and...
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ISBN:
(数字)9781468474046
ISBN:
(纸本)9781850912538
After two decades, data processing has finally, and probably forever, found its niche among civil engineering and construction (CEC) professionnals, through word processors, digitizing tables, management software, and increasingly via drawing software and computer-aided design (CAD), recently, robots have even started invading work sites. What are the main trends of CAD and robotics in the field of architecture and civil enginee ring? What type of R&D effort do university and industrial laboratories undertake to devise the professional software that will be on the market in the next three to five years? These are the issues which will be addressed during this symposium. To this effect, we have planned concurrently an equipment and software show, as well as a twofold conference. Robotic is just starting in the field of civil engineering and construction. A pioneer, the Civil engineering Departement of Carnegie-Mellon University, in the United States, organized the first two international symposia, in 1984 and 1985 in Pittsburgh. This is the third meeting on the subject (this year, however, we have also included CAD). It constitutes the first large international symposium where CAD experts, specialists in architecture and CEC robotics will meet. From this standpoint, it should be an ideal forum for exchanging views and expe riences on a wide range of topics, and we hope it will give rise to novel applications and new syntheses. This symposium is intented for scientists, teachers, students and also for manufacturers and all CEC professionals.
Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detecti...
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Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed. Aim: This study aims to complement existing research with an extensive catalog of the techniques and monitoring data used for detecting anomalies affecting the performance or reliability of cloud-based software systems that have been developed and/or evaluated in a real-world context. Method: We perform a systematic mapping study to examine the literature on anomaly detection in cloud-based systems, particularly focusing on the usage of real-world monitoring data, with the aim of identifying key data categories, tools, data preprocessing, and anomaly detection techniques. Results: Based on a review of 104 papers, we categorize monitoring data by structure, types, and origins and the tools used for data collection and processing. We offer a comprehensive overview of data preprocessing and anomaly detection techniques mapped to different data categories. Our findings highlight practical challenges and considerations in applying these techniques in real-world cloud environments. Conclusion: The findings help practitioners and researchers identify relevant data categories and select appropriate data preprocessing and anomaly detection techniques for their specific operational environments, which is important for improving the reliability and performance of cloud-based systems.
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