Type-driven natural language analysis

The purpose of this thesis is in showing how recent developments in logic programming can be exploited to encode in a computational environment the features of certain linguistic theories. We are in this way able to make available for the purpose of natural language processing sophisticated capabilities of linguistic analysis directly justified by well developed grammatical frameworks. More specifically, we exploit hypothetical reasoning, recently proposed as one of the possible directions to widen logic programming, to account for the syntax of filler-gap dependencies along the lines of linguistic theories such as Generalized Phrase Structure Grammar and Categorial Grammar. Moreover, we make use, for the purpose of semantic analysis of the same kind of phenomena, of another recently proposed extension, interestingly related to the previous one, namely the idea of replacing first-order terms with the more expressive λ-terms of λ-Calculus

Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research

This study evaluates the GPT-4 Large Language Model's abductive reasoning in complex fields like medical diagnostics, criminology, and cosmology. Using an interactive interview format, the AI assistant demonstrated reliability in generating and selecting hypotheses. It inferred plausible medical diagnoses based on patient data and provided potential causes and explanations in criminology and cosmology. The results highlight the potential of LLMs in complex problem-solving and the need for further research to maximize their practical applications.Comment: The article is 12 pages long and has one figure. It also includes a link to some ChatGPT dialogues that show the experiments that support the article's findings. The article will be published in V. Bambini and C. Barattieri di San Pietro (eds.), Sistemi Intelligenti, Special Section "Multidisciplinary perspectives on ChatGPT and the family of Large Language Models

Listening to what the system tells us: Innovative auditing for distributed systems

IntroductionIn recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence.MethodsTo develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system.ResultsWe have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains.DiscussionThe proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems

Constraint-based protocols for distributed problem solving

AbstractDistributed Problem Solving (DPS) approaches decompose problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for solving problems characterized by many interdependencies among subproblems in the context of parallel and distributed architectures. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS where constraints define local problem solving and the exchange of information among agents declaratively. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different problems and we show how the framework applies to simple yet generalizable examples

Blockchain and Cryptocurrencies: a Classification and Comparison of Architecture Drivers

Blockchain is a decentralized transaction and data management solution, the technological leap behind the success of Bitcoin and other cryptocurrencies. As the variety of existing blockchains and distributed ledgers continues to increase, adopters should focus on selecting the solution that best fits their needs and the requirements of their decentralized applications, rather than developing yet another blockchain from scratch. In this paper we present a conceptual framework to aid software architects, developers, and decision makers to adopt the right blockchain technology. The framework exposes the interrelation between technological decisions and architectural features, capturing the knowledge from existing academic literature, industrial products, technical forums/blogs, and experts' feedback. We empirically show the applicability of our framework by dissecting the platforms behind Bitcoin and other top 10 cryptocurrencies, aided by a focus group with researchers and industry practitioners. Then, we leverage the framework together with key notions of the Architectural Tradeoff Analysis Method (ATAM) to analyze four real-world blockchain case studies from industry and academia. Results shown that applying our framework leads to a deeper understanding of the architectural tradeoffs, allowing to assess technologies more objectively and select the one that best fit developers needs, ultimately cutting costs, reducing time-to-market and accelerating return on investment.Comment: Accepted for publication at journal Concurrency and Computation: Practice and Experience. Special Issue on distributed large scale applications and environment