Experimenting with Constraint Programming Techniques in Artificial Intelligence: Automated System Design and Verification of Neural Networks

This thesis focuses on the application of Constraint Satisfaction and Optimization techniques in two Artificial Intelligence (AI) domains: automated design of elevator systems and verification of Neural Networks (NNs). The three main areas of interest for my work are (i) the languages for defining the constraints for the systems, (ii) the algorithms and encodings that enable solving the problems considered and (iii) the tools that implement such algorithms. Given the expressivity of the domain description languages and the availability of effective tools, several problems in diverse application fields have been solved successfully using constraint satisfaction techniques. The two case studies herewith presented are no exception, even if they entail different challenges in the adoption of such techniques. Automated design of elevator systems not only requires encoding of feasibility (hard) constraints, but should also take into account design preferences, which can be expressed in terms of cost functions whose optimal or near-optimal value characterizes “good” design choices versus “poor” ones. Verification of NNs (and other machine-learned implements) requires solving large-scale constraint problems which may become the main bottlenecks in the overall verification procedure. This thesis proposes some ideas for tackling such challenges, including encoding techniques for automated design problems and new algorithms for handling the optimization problems arising from verification of NNs. The proposed algorithms and techniques are evaluated experimentally by developing tools that are made available to the research community for further evaluation and improvement

Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control

Formal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this pa- per we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to ver- ify various safety properties using the tool NEVER2, a new framework that integrates learning and verification in a sin- gle easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning

In-vivo proximal monitoring system for plant water stress and biological activity based on stem electrical impedance

Population growth and global warming are the main threats to food production. Food security, producing enough food for the entire population, is becoming harder, and new strategies must be applied. Smart agriculture tackles this problem by integrating field sensors and data with the farmers’ knowledge to increase crop yield and reduce resource waste.This paper proposes a system to monitor the plant water stress status. This system monitors the plant directly and does not rely on environmental sensors. Acquired data are sent to a remote server thanks to LoRa communication. The designed system is low-power and relies on a single battery with more than five years of expected lifetime. The system monitors the trunk electrical impedance of plants thanks to a relaxation oscillator with a portion of the trunk in the feedback loop. This way, changes in the impedance are reflected in changes in the oscillator frequency.Two systems were installed directly in the fields and connected to apple trees. Statistical analyses were performed on the acquired data. The correlation between the trunk frequency values and the soil water potential is above 75% for both plants.The proposed system is low-power and low-cost and could be directly adopted in the fields. It can detect the water status of plants directly, avoiding environmental sensors

A “Plant-Wearable System” for Its Health Monitoring by Intra- and Interplant Communication

A step forward in smart agriculture is moving to direct monitoring plants and crops instead of their environment. Understanding plant status is crucial in improving food production and reducing the usage of water and chemicals in agriculture. Here, we propose a “plant-wearable,” low-cost, and low-power method to measure in-vivo green plant stem frequency as the indicator for plant watering stress status. Our method is based on measuring the frequency of a digital signal obtained with a relaxation oscillator where the plant is a part of the feedback loop. The frequency was correlated with the soil water potential, used as a critical indicator of plant water stress, and an 85% correlation was found. In this way, the measuring system matches all the requirements of smart agriculture and Internet of Things (IoT): ultra-low-cost, low-complexity, ultra-low-power, and small sizes, introducing the concept of wearability in plant monitoring. The proposed solution exploits the plant and the soil as a communication channel: the signal carrying the plant watering stress status information is transmitted to a receiving system connected to a different plant. The system's current consumption is lower than 50 μμ A during the transmission in the plant and 40 mA for wireless communication. During inactivity periods, the total current consumption is lower than 15 μμ A. Another important aspect is that the system has to be energy autonomous. Our proposal is based on energy harvesting solutions from multiple sources: solar cells and plant microbial fuel cells. This way, the system is batteryless, thanks to supercapacitors as a storage element. The system can be deployed in the fields and used to monitor plants directly in their environment

Managing Phytophthora crown and root rot on tomato by pre-plant treatments with biocontrol agents, resistance inducers, organic and mineral fertilizers under nursery conditions

Five trials were carried out under greenhouse conditions to test the efficacy of spray programmes based on biocontrol agents, phosphite-based fertilizers and a chemical inducer of resistance (acibenzolar-S-methyl, phosethyl-Al) to control crown and root rot of tomato incited by Phytophthora nicotianae. The best disease control, under high disease pressure resulting from artificial inoculation, was obtained with three pre-plant leaf sprays at 7 d intervals with acibenzolar-S-methyl and with two mineral phosphite-based fertilizers. The disease reduction achieved was similar to that obtained with a single application of azoxystrobin and metalaxyl-M. Phosetyl-Al and the biocontrol agents Glomus spp. + Bacillus megaterium + Trichoderma, B. subtilis QST713, B. velezensis IT45 and the mixture T. asperellum ICC012 + T. gamsii ICC080 provided a partial disease control. Brassica carinata pellets did not control the disease