A Unified Framework for Planning in Adversarial and Cooperative Environments

Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or clarify the next actions in a plan when the goal is known to the observer. We present theoretical results on the complexity analysis of our problems. We demonstrate the execution of obfuscated and legible plans in a cooking domain using a physical robot Fetch. We also provide an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains.Comment: 8 pages, 2 figure

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

SUPPLY CHAIN MANAGEMENT AND THE ROMANIAN TRANSITION

Supply Chain Management (SCM), defined here as the construction of productive systems spanning over organizational borders with suppliers and customers and integrated via human-based and information technology systems to satisfy final customer requirements, is introduced as a key concept to accelerate Romania’s economic transition as it approaches EU membership, as well as to develop a modern supplier network. We introduce SCM from a system perspective along three broad areas: input, operations, output and system integration activities. We close by introducing constraints to SCM implementation in Romania. The first major constraint involves a lack of appropriate physical and human capital. Modernization of antiquated equipment and training employees in modern operations practices are prime requisites. The second major constraint, and perhaps the more difficult to change, deals with a lack of social capital among Romanian firms and adapting to appropriate managerial and worker values and attitudes.Supply Chain Management; Social Capital; Transition Economy; Economic Development.

Supply chain management and the Romanian transition

Supply Chain Management (SCM), defined here as the construction of productive systems spanning over organizational borders with suppliers and customers and integrated via humanbased and information technology systems to satisfy final customer requirements, is introduced as a key concept to accelerate Romania’s economic transition as it approaches EU membership, as well as to develop a modern supplier network. We introduce SCM from a system perspective along three broad areas: input, operations, output and system integration activities. We close by introducing constraints to SCM implementation in Romania. The first major constraint involves a lack of appropriate physical and human capital. Modernization of antiquated equipment and training employees in modern operations practices are prime requisites. The second major constraint, and perhaps the more difficult to change, deals with a lack of social capital among Romanian firms and adapting to appropriate managerial and worker values and attitudes.Supply Chain Management; Social Capital; Transition Economy; Economic Development

The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation