# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 1 Introduction  
Today, the paradigm of programming smart objects is evolving to incorporate social aspects, driven by the necessity for these objects to interact dynamically to perform complex tasks. These tasks may involve exchanging services, making requests, or negotiating contracts [1, 2, 3]. The representation of social interactions between software entities has naturally emerged within the field of cooperative smart objects [4, 5], where leveraging social characteristics enables collaborative behaviors. Various approaches have been introduced to enhance collaboration among smart objects within their communities [6]. Additionally, in competitive environments such as e-commerce, several models have been developed [7, 8] to equip smart objects with social capabilities that improve their ability to achieve specific goals. Across these models, the need to effectively represent trustworthiness among community members remains a consistent requirement. This necessity has led to thedevelopment of trust-based models that address different dimensions of trust relationships. Such models are typically designed to enhance smart object performance, particularly in IoT environments, where objects operate within vast networks and must constantly share information. When two smart objects interact, the one requesting a service is termed the trustor, while the one providing it is the trustee. A trust relationship between two smart objects often spans multiple dimensions. The term dimension refers to the specific perspective used to evaluate the interaction. Common trust dimensions include attributes such as competence, honesty, security, reliability, and expertise, alongside others that depend on the specific context. These dimensions represent subjective measures that each smart object independently calculates based on its perception of its surroundings. However, in some cases, an additional dimension--reputation--is necessary. This is where the community collectively assigns a trust value to a trustee. Reputation becomes particularly relevant when a smart object \(o_{1}\) lacks direct knowledge of another smart object \(o_{2}\) and must instead rely on the community's collective assessment of \(o_{2}\)'s trustworthiness. Various trust and reputation models have been proposed for multi-smart object communities [9; 10]. Most of these approaches quantify trust using scalar measures, integrating them into synthetic trustworthiness indicators. However, a key limitation of these models is their reliance on simple scalar values, often structured as trust vectors. For example, the trust of a smart object \(o_{1}\) toward another object \(o_{2}\) might be represented by the vector \(trust_{o_{1}o_{2}}=[honesty,reliability,expertise,...]\). These trust vector elements are often interdependent rather than fully independent. For instance, a smart object might infer honesty from reliability, assuming that a reliable partner is also likely to be honest to some degree, factoring in both internal and external conditions. Our study does not introduce new quantitative trust measures but instead presents a model capable of representing scenarios where a smart object lacks direct knowledge of a trust dimension, such as reliability, and infers it from another, like honesty. The proposed model captures the interdependencies among multiple trust dimensions. To address this, we propose a trust and reputation model for social smart object communities, where the trust a smart object \(o_{1}\) perceives toward another \(o_{2}\) is not represented as a vector of independent values. Instead, it is modeled as a directed, weighted graph, where nodes represent trust dimensions (termed _trust aspects_), and edges capture their interdependencies. This approach allows smart objects to infer unknown trust dimensions based on available information from other related dimensions. The proposed model, named T-pattern [11], is designed to capture interdependent trust dimensions. It employs a formalism similar to logical rules.