# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 2 Related Works
The trained deep belief network (DBN) demonstrated superior intrusion detection capabilities compared to other approaches through experimental results. Trust is also critical in multi-agent systems (MASs), particularly in collaborative agent environments, including IoT applications [24; 25; 26; 27]. For example, Ref. [10] discusses a social IoT MAS designed to safeguard trust relationships within the IoT community while minimizing the influence of malicious nodes. Trust evaluation schemes can further enhance federated learning by managing direct trust evidence and recommended trust information, as seen in [28]. Regarding the T-pattern model approach discussed in this paper, it incorporates an \"ontology\" as part of its logical framework. An ontology is defined as a set of entity descriptions (e.g., classes, relations, and functions) and explicit assumptions represented through a vocabulary that describes reality with a clear and consistent meaning. This framework formalizes knowledge, whether for an agent or a community of agents, using first-order logic where vocabulary items appear as unary (concepts) or binary (relationships) predicates [29]. Notably, two ontologies may use different vocabularies while sharing the same conceptualization. In agent-based scenarios, ontologies can serve as the foundation of knowledge representation, encompassing key concepts, properties, and relationships, as well as conceptual schemas. This is similar to the framework provided by the JAva DEvelopment Framework (JADE) [30], which models predicates, terms, concepts, actions, and more--akin to the components of agent communication messages. In this context, we adopt ontologies to represent an agent's \"viewpoint\" regarding interests and behaviors (either its own or those of its owner), typically referred to as a model. This model is adaptable to various frameworks where the agent may operate. In the past literature, many research work on trust ontologies in multi-agent systems has been proposed. For example, in [31], the authors survey and classify thirteen computational trust models by the trust decision input factors, using such an analysis to create a new comprehensive ontology for trust to facilitate interaction between business systems. Moreover, in [32], it is recognized that an important application area of the Semantic Web is ontology mapping, where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if trust is not managed effectively between the different sources. In this paper, the authors propose a solution for managing trust between contradicting beliefs in similarities for ontology mapping based on the fuzzy voting model. In [33], an ontology-based multi-agent virtual enterprise (OMAVE) system is proposed to help SMEs shift from the classical trend of manufacturing part pieces to producing high-value-added, high-tech, innovative products. Furthermore, in [34], the authors introduce an in-depth ontological analysis of the notion of trust, grounded in the Unified Foundational Ontology, and they propose a concrete artifact, namely, the Reference Ontology for Trust, in which the general concept of trust is characterized. In this work, the authors distinguish between two types of trust, namely, social trust and institution-based trust, and they also represent the emergence of risk from trust relations. A systematic review on trust-based negotiation in multi-agent systems (MASs) has been performed in [35], through a bibliometric analysis over the past 25 years of research publications, on three of the most popular scientific databases (Google Scholar, Scopus, and Web of Science). This analysis reveals that this research topic is regaining interest, after some oscillating years, and the impact of its contributions is equivalent to other equally important research variants like ontology and argumentation (in a negotiation scenario).