An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

Secure Multi-Robot Adaptive Information Sampling with Continuous, Periodic and Opportunistic Connectivity

Multi-robot teams are an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, natural disaster aftermath surveying, and pollution tracking. In a coordinated multi-robot information sampling scenario, robots share their collected information amongst one another to form better predictions. These robot teams are often assembled from untrusted devices, making the verification of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of continuous, periodic, or opportunistic connectivity and are limited in their energy budget and computational power. In this thesis, we study how to secure the information being shared in a multi-robot network against integrity attacks and the cost of integrating such techniques. We propose a blockchain-based information sharing protocol that allows robots to reject fake data injection by a malicious entity. However, optimal information sampling is a resource-intensive technique, as are the popular blockchain-based consensus protocols. Therefore, we also study its impact on the execution time of the sampling algorithm, which affects the energy spent. We propose algorithms that build on blockchain technology to address the data integrity problem, but also take into account the limitations of the robots’ resources and communication. We evaluate the proposed algorithms along the perspective of the trade-offs between data integrity, model accuracy, and time consumption under continuous, periodic, and opportunistic connectivity

Hitch Hiker 2.0: a binding model with flexible data aggregation for the Internet-of-Things

Wireless communication plays a critical role in determining the lifetime of Internet-of-Things (IoT) systems. Data aggregation approaches have been widely used to enhance the performance of IoT applications. Such approaches reduce the number of packets that are transmitted by combining multiple packets into one transmission unit, thereby minimising energy consumption, collisions and congestion. However, current data aggregation schemes restrict developers to a specific network structure or cannot handle multi-hop data aggregation. In this paper, we propose Hitch Hiker 2.0, a component binding model that provides support for multi-hop data aggregation. Hitch Hiker uses component meta-data to discover remote component bindings and to construct a multi-hop overlay network within the free payload space of existing traffic flows. Hitch Hiker 2.0 provides end-to-end routing of low-priority traffic while using only a small fraction of the energy of standard communication. This paper extends upon our previous work by incorporating new mechanisms for decentralised route discovery and providing additional application case studies and evaluation. We have developed a prototype implementation of Hitch Hiker for the LooCI component model. Our evaluation shows that Hitch Hiker consumes minimal resources and that using Hitch Hiker to deliver low-priority traffic reduces energy consumption by up to 32 %

Planning-Aware Communication for Decentralised Multi-Robot Coordination

© 2018 IEEE. We present an algorithm for selecting when to communicate during online planning phases of coordinated multi-robot missions. The key idea is that a robot decides to request communication from another robot by reasoning over the predicted information value of communication messages over a sliding time-horizon, where communication messages are probability distributions over action sequences. We formulate this problem in the context of the recently proposed decentralised Monte Carlo tree search (Dec-MCTS) algorithm for online, decentralised multi-robot coordination. We propose a particle filter for predicting the information value, and a polynomial-time belief-space planning algorithm for finding the optimal communication schedules in an online and decentralised manner. We evaluate the benefit of informative communication planning for a multi-robot information gathering scenario with 8 simulated robots. Our results show reductions in channel utilisation of up to four-fifths with surprisingly little impact on coordination performance

Information Acquisition with Sensing Robots: Algorithms and Error Bounds

Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014 IEEE International Conference on Robotics and Automatio