Andrea Omicini

University of Bologna | UNIBO · Department of Computer Science and Engineering DISI

We propose a novel method to inject symbolic knowledge in form of Datalog formulae into neural networks (NN), called KINS (Knowledge Injection via Network Structuring). The idea behind our method is to extend NN internal structure with ad-hoc layers built out the injected symbolic knowledge. KINS does not constrain NN to any specific architecture,...

Modern distributed systems require communicating agents to agree on a shared, formal semantics for the data they exchange and operate upon. The Semantic Web offers tools to encode semantics in the form of ontologies, where data is represented in the form of knowledge graphs (KG). Applying such tools to intelligent agents equipped with machine learn...

A long-standing ambition in artificial intelligence is to integrate predictors’ inductive features (i.e., learning from examples) with deductive capabilities (i.e., drawing inferences from symbolic knowledge). Many methods in the literature support injection of symbolic knowledge into predictors, generally following the purpose of attaining better...

A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) predictors – such as neural networks – by extracting symbolic knowledge out of them, in the form of either rule lists or decision trees. By acting as a surrogate model, the extracted knowledge aims at revealing the inner working of the black box, thus...

In this paper we present the computational model of Arg2P, a logic-based argumentation framework for defeasible reasoning and agent conversation particularly suitable for explaining agent intelligent behaviours. The model is reified as the Arg2P technology, which is presented and discussed both from an architectural and a technological perspective...

This work defines a burden of persuasion meta-argumentation model interpreting the burden as a set of meta-arguments. Bimodal graphs are exploited to define a meta level (dealing with the burden) and an object level (dealing with standard arguments). Finally, an example in the law domain addressing the problem of burden inversion is discussed in de...

The application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the de-facto technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community...

To date, logic-based technologies are either built on top or as extensions of the Prolog language, mostly working as monolithic solutions tailored upon specific inference procedures, unification mechanisms, or knowledge representation techniques. Instead, to maximise their impact, logic-based technologies should support and enable the general-purpo...

Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment ana...

Knowledge-extraction methods are applied to ML-based predictors to attain explainable representations of their operation when the lack of interpretable results constitutes a problem. Several algorithms have been proposed for knowledge extraction, mostly focusing on the extraction of either lists or trees of rules. Yet, most of them only support sup...

Recently, the Deep Learning (DL) research community has focused on developing efficient and highly performing Neural Networks (NN). Meanwhile, the eXplainable AI (XAI) research community has focused on making Machine Learning (ML) and Deep Learning methods interpretable and transparent, seeking explainability. This work is a preliminary study on th...

Knowledge extraction methods are applied to ML-based predictors to attain explainable representations of their functioning when the lack of interpretable results constitutes a problem. Several algorithms have been proposed for knowledge extraction, mostly focusing on the extraction of either lists or trees of rules. Yet, most of them only support s...

Recently, the Deep Learning (DL) research community has focused on developing efficient and highly performing Neural Networks (NN). Meanwhile, the eXplainable AI (XAI) research community has fo-cused on making Machine Learning (ML) and Deep Learning methods interpretable and transparent, seeking explainability. This work is a preliminary study on t...

Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning(ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment anal...

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques.

The ability to lazily manipulate long or infinite streams of data is an essential feature in the era of data-driven artificial intelligence. Yet, logic programming technologies currently fall short when it comes to handling long or infinite streams of data. In this paper, we discuss how Prolog can be reinterpreted as a stream processing tool, and r...

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we e...

Since most complex software systems are intrinsically multi-paradigm, their engineering is a challenging issue. Multi-paradigm modeling (MPM) aims at facing the challenge by providing concepts and tools promoting the integration of models, abstractions, technologies, and methods originating from diverse computational paradigms. In this chapter, the...

Service self-composition is a well-understood research area focusing on service-based applications providing new services by automatically combining pre-existing ones. In this paper we focus on tuple-based coordination, and propose a solution leveraging logic tuples and tuple spaces to support semantic self-composition for services. A full-stack de...

The blockchain concept and technology are impacting many different research and application fields; hence, many are looking at the blockchain as a chance to solve long-standing problems or gain novel benefits. In the agent community several authors are proposing their own combination of agent-oriented technology and blockchain to address both old a...

Service self-composition is a well-understood research area focusing on service-based applications providing new services by automatically combining pre-existing ones. In this paper we focus on tuple-based coordination, and propose a solution leveraging logic tuples and tuple spaces to support semantic self-composition for services. A full-stack de...

Mainstream programming languages nowadays tends to be more and more multi-paradigm ones, by integrating diverse programming paradigms-e.g., object-oriented programming (OOP) and functional programming (FP). Logic-programming (LP) is a successful paradigm that has contributed to many relevant results in the areas of symbolic AI and multi-agent syste...

The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) understandable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which lev...

The more intelligent systems based on sub-symbolic techniques pervade our everyday lives, the less human can understand them. This is why symbolic approaches are getting more and more attention in the general effort to make AI interpretable, explainable, and trustable. Understanding the current state of the art of AI techniques integrating symbolic...

Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on th...

We are pleased to see the publication of this special issue focusing on the design and implementation of new methods, techniques, and models that adapt or hybridize findings from Distributed Optimization, Multi-Agent Systems, Network Science, and Distributed Computing, and facilitate distributed/parallel/multi-agent decision-making and coordination...

One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature...

Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation—which are often (and mistakenly) used interchan...

Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation-which are often (and mistakenly) used interchan...

We propose an abstract framework for XAI based on MAS encompassing the main definitions and results from the literature, focussing on the key notions of interpretation and explanation. KEYWORDS XAI; Multi-Agent Systems; Abstract Framework ACM Reference Format:

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic...

Complexity of intra- and inter-systems interactions is steadily increasing in modern application scenarios such as the Internet of Things, therefore coordination technologies are required to take a crucial step forward towards full maturity. In this paper we look back at the history of the COORDINATION conference series with the goal of shedding li...

A common use case for blockchain smart contracts (SC) is that of governing interaction amongst mutually untrusted parties, by automatically enforcing rules for interaction. However, while many contributions in the literature assess SC computational expressiveness, an evaluation of their power in terms of coordination (i.e., governing interaction) i...

In this paper we focus on the expressiveness of smart contracts (SC) and its role in blockchain technologies (BCT), by presenting Tenderfone, a prototypical blockchain platform providing SC as pro-active, time-aware, and asynchronous entities.

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored...

In the context of the Internet of Things (IoT), intelligent systems (IS) are increasingly relying on Machine Learning (ML) techniques. Given the opaqueness of most ML techniques, however, humans have to rely on their intuition to fully understand the IS outcomes: helping them is the target of eXplainable Artificial Intelligence (XAI). Current solut...

The Intelligent Edge computing paradigm is playing a major role in the design and development of Cyber-Physical and Cloud Systems (CPCS), extending the Cloud and overcoming its limitations so as to better address the issues related with the physical dimension of data—and therefore of the data-aware intelligence (such as context-awareness and real-t...

Features of blockchain technology (BCT) such as decentralisation, trust, fault tolerance, and accountability, are of paramount importance for multi-agent systems (MAS). In this paper we argue that a principled approach to MAS-BCT integration cannot overlook the foundational character of agency—that is, autonomy. Accordingly, we present a custom BCT...

Many research works apply blockchain technologies to several different application domains ranging from supply chain and logistics to healthcare and real-estate. There, nevertheless, the blockchain performs the same two core tasks: identity management and asset tracking. In this paper we analyse how the blockchain can be exploited beyond these trad...

Pervasiveness of ICT resources along with the promise of ubiquitous intelligence is pushing hard both our demand and our fears of AI: demand mandates for the ability to inject (micro) intelligence ubiquitously, fears compel the behaviour of intelligent systems to be observable, explainable, and accountable. Whereas the first wave of the new “AI Era...

Multi-agent systems (MAS) allow and promote the development of distributed and intelligent applications in complex and dynamic environments. Applications of this kind have a crucial role in our everyday life, as witnessed by the broad range of domains they are deployed to—such as manufacturing, management sciences, e-commerce, biotechnology, etc. D...

The widespread adoption of digital technologies and computational devices, along with their pervasiveness in our everyday life, is going to make them hugely impact over all key processes in human societies-including the democratic one. The last decade has witnessed the emergence of many tools and platforms for digital democracy. However, also becau...

Aggregate Computing is a promising paradigm for coordinating large numbers of possibly situated devices, typical of scenarios related to the Internet of Things, smart cities, drone coordination, and mass urban events. Currently, little work has been devoted to study and improve security in aggregate programs, and existing works focus solely on appl...

In this paper we present the ReSpecTX language, toolchain, and standard library as a first step of a path aimed at closing the gap between coordination languages – mostly a prerogative of the academic realm until now – and their industrial counterparts. Since the limited adoption of coordination languages within the industrial realm is also due to...

In the era of Big Data and IoT, successful systems have to be designed to discover, store, process, learn, analyse, and predict from a massive amount of data—in short, they have to behave intelligently. Despite the success of non-symbolic techniques such as deep learning, symbolic approaches to machine intelligence still have a role to play in orde...

New generations of distributed systems are opening novel perspectives for logic programming (LP): On the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper, we introduce the notion of Logic Progra...

In order to enable logic programming to deal with the diversity of pervasive systems, where many heterogeneous, domain-specific computational models could benefit from the power of symbolic computation, we explore the expressive power of labelled systems. To this end, we define a new notion of truth for logic programs extended with labelled variabl...

New generations of distributed systems are opening novel perspectives for logic programming (LP): on the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper we introduce the notion of Logic Program...

Since complexity of inter- and intra-systems interactions is steadily increasing in modern application scenarios (e.g., the IoT), coordination technologies are required to take a crucial step towards maturity. In this paper we look back at the history of the COORDINATION conference in order to shed light on the current status of the coordination te...

We introduce Spatial Tuples, an extension of the basic tuple‐based model for distributed multi‐agent system coordination where (a) tuples are conceptually placed in regions of the physical world and possibly move anchored to a mobile computational device, (b) the behaviour of standard Linda coordination primitives is extended so as to depend on the...

The lack of a suitable toolchain for programming the interaction space with coordination languages hinders their adoption in the industry, and limits their application as core calculus, proof-of-concept frameworks, or rapid prototyping/simulation environments. In this paper we present the \(\texttt {ReSpecT}\mathbb {X}\) language and toolchain as a...

This Deliverable overviews the main issues and steps faced during the design and development of the BISON project in order not only to cope with the ethical and legal issues raised by big speech data analytics, but more generally to devise an inter-disciplinary design & development methodology that aims at devising a law-abiding system, where the l...

We introduce Spatial Tuples, an extension of the basic tuplebased model for distributed multi-agent system coordination where (i) tuples are conceptually placed in the physical world and possibly move, (ii) the behaviour of coordination primitives may depend on the spatial properties of the coordinating agents, and (iii) the tuple space can be conc...

The impact of mobile technologies on healthcare is particularly evident in the case of self-management of chronic diseases, where they can decrease spending and improve life quality of patients. We propose the adoption of agent-based modeling and simulation techniques as built-in tools to dynamically monitor the state of patient health and provide...

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computationalbiology i.e., to modelling and simulating biological systems by means of computational models,methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the fieldof multicellular systems biology is explored, f...

Large-scale socio-technical systems (STS) inextricably interconnect individual–e.g., the right to privacy–, social–e.g., the effectiveness of organisational processes–, and technology issues—e.g., the software engineering process. As a result, the design of the complex software infrastructure involves also non-technological aspects such as the lega...

The peculiar features of emerging large-scale ubicomp systems require novel approaches to coordinate their overall activities and functionalities in a decentralized way. In this position paper, we introduce a few representative application scenarios that calls for decentralized and adaptive coordination, and discuss some key-challenges to be faced...

Whereas Multi-Agent Based Simulation (MABS) is emerging as a reference approach for complex system simulation, the event-driven approach of Discrete-Event Simulation (DES) is the most used approach in the simulation mainstream. In this paper we elaborate on two intuitions: (i) event-based systems and multi-agent systems are amenable of a coherent i...

Most of the emerging software-intensive systems nowadays are very large-scale ones, and inherently socio-technical. In this introduction to the special section on 'Coordination in Large-Scale Socio-Technical Systems' we argue that the peculiar features of such emerging systems (up to millions of interacting components, lacking central control, mixi...

In the discussion on autonomous systems and their regulation, distinct concepts of autonomy are put forward. For instance, the US Military Defense Science Board provides the following definition: ‘Autonomy is a capability (or a set of capabilities) that enables a particular action of a system to be automatic or, within programmed boundaries, “self-...

In spite of the growing influence of agent-based models and technologies, the event-based architectural style is still prevalent in the design of large-scale distributed applications. In this paper we discuss the role of environment in both EBS and MAS, and show how it could be used as a starting point for reconciling agent-based and event-based ab...

Some of the most peculiar traits of socio-technical KIE (knowledge-intensive environments) – such as unpredictability of agents’ behaviour, ever-growing amount of information to manage, fast-paced production/consumption – tangle coordination of information, by affecting, e.g., reachability by knowledge prosumers and manageability by the IT infrastr...

While event-based architectural style has become prevalent for large-scale distributed applications, multi-agent systems seemingly provide the most viable abstractions to deal with complex distributed systems. In this position paper we discuss the role of coordination abstractions as a basic brick for a unifying conceptual framework for agent-based...

Agent activities and environment change are what make things happen in a multi-agent system (MAS). Complexity in a MAS comes from non-trivial dependencies between activities (social interaction), and between activities and environment change (situated interaction). As they are used to manage social (agent-agent) dependencies, coordination artefacts...

Objective and subjective approaches to coordination constitute two complementary approaches, which, being both essential in MAS engineering, require to be suitably integrated. In this paper, we (i) observe that a successful integration depends on the models of autonomy and coordination promoted by agent technologies, (ii) suggest that ignoring the...

Enabling and controlling the elasticity of cloud computing applications is a challenging issue: programming directives have been introduced to delegate elasticity control to the infrastructure as well as to separate elasticity control from the application logic. Since coordination models provide a general approach to managing interaction, and elast...

Multi-agent systems (MAS) provide a well-founded approach to the engineering of situated systems, where governing the interaction of a multiplicity of autonomous, distributed components with the environment represents one of the most critical issues. By interpreting situatedness as a coordination issue, in this paper we describe the TuCSoN coordina...

Agent-based technologies embed solutions for critical issues in agent-oriented software engineering. In this paper we describe the coordination-based approach to MAS situatedness as promoted by the TuCSoN middleware, by sketching the steps of an agent-oriented methodology from the TuCSoN meta-model down to the TuCSoN programming environment.

The SODA methodology deals with MAS analysis and design, and focuses on critical issues such as agent coordination and MAS-environment interaction. After its first formulation, in order to further meet the needs of complex MAS engineering, SODA was extended to embody both the layering principle and the Agents & Artifacts (A&A) meta-model. As a resu...

Gaia was the first complete methodology proposed for the development of multi-agent systems (MASs), and was subsequently improved to designing and building systems in complex, open environments. Gaia focuses on the use of the organizational abstractions to drive the analysis and design of MAS. Gaia models both the macro (social) aspects and the mic...

A network is a mathematical object consisting of a set of points that are connected to each other in some fashion by lines. It turns out this simple description corresponds to a bewildering array of systems in the real world, ranging from technological ones such as the Internet and World Wide Web, biological networks such as that of connections of...

Spatial issues are essential in new classes of complex software systems, such as pervasive, multi-agent, and self-organising ones. Understanding the basic mechanisms of spatial coordination is a fundamental issue for coordination models and languages in order to deal with such systems, governing situated interaction in the spatio-temporal fabric. A...

The next generation of computational systems is going to mix up pervasive scenarios with cloud computing, with both intelligent and non-intelligent agents working as the reference component abstractions. A uniform set of MAS abstractions expressive enough to deal with both embodied and disembodied computation is required, in particular when dealing...

Complex computational systems — such as pervasive, adaptive, and self-organising ones — typically rely on simple yet expressive coordination mechanisms: this is why coordination models and languages can be exploited as the sources of the essential abstractions and mechanisms to build such systems. While the features of tuple-based models make them...

We advocate the role of tuple-based coordination languages as effective tools for event-driven programming of situated multi-agent systems (MAS). By focussing on logic-based coordination artefacts, we discuss the benefits of exploiting ReSpecT tuple centres as event-driven abstractions for MAS coordination.

Complex systems of any kind are characterised by autonomous components interacting with each other in a non-trivial way. In this paper we discuss how the views on complexity are evolving in fields like physics, social sciences, and computer science, and – most significantly – how they are converging. In particular, we focus on the role of interacti...

In the next decades, the emergence of complex intelligent systems is going to open a plethora of new opportunities for logic programmers, capable of injecting Prolog programs within adaptive, pervasive, self-organising, knowledge-intensive systems, and integrating them with all the sorts of different programming languages and paradigms, over comput...

Complex software systems modelled as multi-agent systems (MAS) are characterised by activities that are generated either by agents, or by the environment in its most general acceptation—that is, environmental resources and the spatio-temporal fabric. Modelling and engineering complex multi-agent systems (MAS) – such as pervasive, adaptive, and situ...