Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

MAKE-OR-BUY THEORIES: WHERE DO WE STAND?

The aim of this paper is to discuss the state-of-the art and the directions for research on the make-orbuy problem. After thirty years of research efforts, we now have numerous contributions explaining different aspects of the nature and existence of the firm. The search for a unified theory, however, still remains, at a theoretical level, a challenge. The task is not easy, perhaps because the theory of the firm develops along two different strands, one analyzing the factors influencing the boundaries, and the other one relating to the internal structure; or because, even inside the same research strand, it is not really easy to grasp the similarities and differences between contributions that have followed one another in rapid succession over the last few years. This paper examines the theories concerning the make-or-buy problem, focusing on recent contributions that have tried to develop a unified framework and emphasizes the role of incomplete contracts as a common and significant trait of the theories discussed

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Regional monopoly and interregional and intraregional competition: the parallel trade in Coca-Cola between Shanghai and Hangzhou in China

This article uses a “principal-agent-subagent” analytical framework and data that were collected from field surveys in China to (1) investigate the nature and causes of the parallel trade in Coca-Cola between Shanghai and Hangzhou and (2) assess the geographic and theoretical implications for the regional monopolies that have been artificially created by Coca-Cola in China. The parallel trade in Coca-Cola is sustained by its intraregional rivalry with Pepsi-Cola in Shanghai, where Coca-Cola (China) (the principal) seeks to maximize its share of the Shanghai soft-drinks market. This goal effectively supersedes the market-division strategy of Coca-Cola (China), since the gap in wholesale prices between the Shanghai and Hangzhou markets is higher than the transaction costs of engaging in parallel trade. The exclusive distributor of Coca-Cola in the Shanghai market (the subagent) makes opportunistic use of a situation in which it does not have to bear the financial consequences of the major residual claimants (the principal and other agents) and has an incentive to enter the nondesignated Coca-Cola market of Hangzhou by crossing the geographic boundary between the two regional monopolies devised by Coca-Cola. The existence of parallel trade in Coca-Cola promotes interregional competition between the Shanghai and Hangzhou bottlers (the agents). This article enhances an understanding of the economic geography of spatial equilibrium, disequilibrium, and quasi-equilibrium of a transnational corporation's distribution system and its artificially created market boundary in China

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented