# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 2 Related Work  
Trust, a subjective judgment, refers to the confidence in some aspect or another of a relationship in most of our reviewed articles [4]. It was first introduced in computer science as a measure of an agent in 1994 [13]. Trust can be classified into three types [14; 15]. The first type of trust refers to truth and belief, which means trusting the behavior or quality of the other object. Next, trust implies that a declaration is correct. Finally, \"Trust as commerce\" indicates the ability or the intention of a buyer to purchase in the future. In this paper, we adopt the first class, that is to say, trust can be interpreted by believing the behavior and quality of the identity. The literature is plentiful with various approaches to trust estimation in MASs [2; 15; 16; 17; 18; 19]. In general, trust should be based on evidence consisting of positive and negative experiences with it [15; 20; 21]. [PERSON], [PERSON] and [PERSON], [PERSON] capture trust in MASs based on the beta probability density functions that rely on the number of negative and positive interactions [2]. The probability expectation is used to express the trust value. The literature [22] provides a systematic study on when it is beneficial to combine direct trust and indirect reputation. TRAVOS [9] models trustworthiness probabilistically through a beta distribution computed from the outcomes of all the interactions a truster has observed, and it uses the probability of successful interaction between two agents to capture the value of trustworthiness. [PERSON], [PERSON], et al. studied trust and reputation in the Internet of Things [23]. In addition, some references evaluate trustworthiness by taking advantage of recent performances. In the literature [12; 24], they achieve trust by evaluating recent performance and historical evaluations. However, trust evaluation might encounter uncertainty deviated from cognitive impairment, inefficient evidence, randomness, and incomplete information [25; 26]. [PERSON] and [PERSON] use evidence theory to present uncertainty for trust estimation [27; 28; 29]. In [PERSON] and [PERSON]'s model [30], they adopt a three-value opinion (belief, disbelief, uncertainty) in the opinion-based model for trust estimation. However, these models are questionable for trust estimation in a MAS with deceptive agents involved. At the same time, most of them define uncertainty by the medium evaluations. However, as explained, uncertainty stems from cognitive impairment, inefficient evidence, randomness, and incomplete information [25, 26]. Therefore, a more exhaustive exploration that exploits the uncertainty needs to be carried out. It is natural and reasonable to query for third-party information whenever the direct experience is insufficient. However, it is risky to make a final decision relying entirely and directly on the gathered information in a distributed MAS, especially with deceptive agents involved. Thus, we need to ensure that the accumulated information is reliable before being used. Therefore, the concept of confidence is adopted to capture the reliability of gathered information [9, 12, 15, 20, 24, 28, 31]. We define confidence as a probability that the trust is applicable, supported by evidence, consistency, credibility, and certainty. However, most of these models only take some aspects into consideration as a separate trust model in MASs. First, some trust models assume that most third -party agents are reliable [2]. Thus, those opinions that are consistent with the majority can be regarded as reliable [2, 32]. Others identify unreliable agents by comparing them with direct experience [9, 28]. Here in this paper, we define this property as **consistency**. Consistency displays the differences or similarities with other evidence. It is helpful, especially for those agents whose trust and certainty are unknown. However, most of our reviewed articles did not pay much attention to consistency for trust estimation in MASs. Credibility is widely applied in distributed MASs to capture confidence.