Logic Programming and Machine Ethics

Transparency is a key requirement for ethical machines. Verified ethical behavior is not enough to establish justified trust in autonomous intelligent agents: it needs to be supported by the ability to explain decisions. Logic Programming (LP) has a great potential for developing such perspective ethical systems, as in fact logic rules are easily comprehensible by humans. Furthermore, LP is able to model causality, which is crucial for ethical decision making.Comment: In Proceedings ICLP 2020, arXiv:2009.09158. Invited paper for the ICLP2020 Panel on "Machine Ethics". arXiv admin note: text overlap with arXiv:1909.0825

Micro-intelligence for the IoT: logic-based models and technologies

Computing is moving towards pervasive, ubiquitous environments in which devices, software agents and services are all expected to seamlessly integrate and cooperate in support of human objectives. An important next step for pervasive computing is the integration of intelligent agents that employ knowledge and reasoning to understand the local context and share this information in support of intelligent applications and interfaces. Such scenarios, characterised by "computation everywhere around us", require on the one hand software components with intelligent behaviour in terms of objectives and context, and on the other their integration so as to produce social intelligence. Logic Programming (LP) has been recognised as a natural paradigm for addressing the needs of distributed intelligence. Yet, the development of novel architectures, in particular in the context Internet of Things (IoT), and the emergence of new domains and potential applications, are creating new research opportunities where LP could be exploited, when suitably coupled with agent technologies and methods so that it can fully develop its potential in the new context. In particular, the LP and its extensions can act as micro-intelligence sources for the IoT world, both at the individual and the social level, provided that they are reconsidered in a renewed architectural vision. Such micro-intelligence sources could deal with the local knowledge of the devices taking into account the domain specificity of each environment. The goal of this thesis is to re-contextualise LP and its extensions in these new domains as a source of micro-intelligence for the IoT world, envisioning a large number of small computational units distributed and situated in the environment, thus promoting the local exploitation of symbolic languages with inference capabilities. The topic is explored in depth and the effectiveness of novel LP models and architectures -and of the corresponding technology- expressing the concept of micro-intelligence is tested

Mining for Useful Association Rules Using the ATMS

Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules

Modeling the Internet of Things: a simulation perspective

This paper deals with the problem of properly simulating the Internet of Things (IoT). Simulating an IoT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the provision of scalable simulation scenarios, that enable the real-time execution of massively populated IoT environments. Attention is given to novel hybrid and multi-level simulation techniques that, when combined with agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches, can provide means to perform highly detailed simulations on demand. To support this claim, we detail a use case concerned with the simulation of vehicular transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High Performance Computing and Simulation (HPCS 2017

Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack

In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required communication load on the band-limited network. We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization. The Byzantine agents are capable of intelligently falsifying their data and manipulating the underlying communication graph by altering their respective control feedback weights. We introduce a decentralized detection framework and analyze its steady-state and transient performances. We propose a way of identifying individual Byzantine neighbors and a learning-based method of estimating the attack parameters. Lastly, we propose learning-based control approaches to mitigate the negative effects of the adversarial attack