# Artificial Subjectivity: Personal Semantic Memory Model for Cognitive Agents
## 2 Related Work
### Proposed Architecture
Three standard aspects were used from the emotion framework [33] such as Agent, AgentAction, Time whereas four aspects Sentiment, AgentEmotion, RecipientEmotion, and Personality added that means at a certain time some event held in which Agent_i performed Action_i with Sentiment_i and Emotion_i showing Personality_i. These aspects contributed to the computation of cognitive value as shown in Equation (6). **The personal semantics formulation phase** consisted of the evaluation module and the personal semantic construction module as shown in Figure 1. In this phase personal semantic memory content and cognitive values were formulated. Each chunk of personal semantics memory has eight aspects of personal semantics Agent_i, Personality_i, Sentiment_i, AttentionObjective_i, AttentionSubjective_i, Mass_i, Confidence_i, Support_i which means that Agent_i has personality_i and the sentiment_i with cognitive value AttentionObjective_i, AttentionSubjective_i, Mass_i, Confidence_i and Support_i. Knowledge graphs were used to represent the personal semantics where the predicate was represented as a node and all the proposed cognitive values were represented as attributes. The list of proposed cognitive values was Attention Objective, Attention Subjective, Mass, Confidence, and Support. These values were initially practiced using the wizard of Oz method [34]. A Mass value was proposed to compute the strength of a chunk of personal semantics that indicates its resistance to change. Details of cognitive values are given under evaluation modules. Each chunk of personal semantics was either added as new amendments using AmendmentLink or updated the existing one in terms of cognitive values. Cognitive values were computed in the evaluation module whereas the constructor module was involved in the representation of the chunk of personal semantic memory. In **Evaluation Module**, \(f_{evaluator}\) was the evaluator function that evaluated the knowledge based on the content space \(C\) and calculated the proposed cognitive values of the specific chunk of personal semantic memory \(C_{PSM}\) as shown in Equation (7) using Equations (9)-(12). \[C_{PSM}=f_{evaluator}(C) \tag{7}\]  
The cognitive values that were used in this work are the Mass, Attention Objective, Attention Subjective, Confidence, and Support. **Mass** was the weight of each personal semantic knowledge node indicated how many supported episodes exist in the episodic memory of the agent. Suppose agent had \(n\) number of supported episodes for the given chunk \(C_{PSM}\) of personal semantic knowledge. Mass was calculated by adding the number of supported episodes and the direct association. The direct association was 1 in case the agent have direct interaction with the individual otherwise it was zero. Mass increased as the number of supported episodes increased symbolized in Equation (8). \[percepts_{p}=f_{perception}(percepts_{s}) \tag{2}\]0  
**Attention Objective**, \(A\)O of a chunk of personal semantics \(C_{PSM}\) was the repetition of the same event \(E\) calculated using Equation (9). If the same event \(E\) occurred repeatedly the Attention Objective increases. A function \(ft\), checked the experience periodically andreturned how many consecutive occurrences exist. Every repeated occurrence incremented the attention objective by 1. \[percepts_{p}=f_{perception}(percepts_{s}) \tag{2}\]1  
**Attention Subjective**, \(AS\) of a chunk of personal semantics \(C_{PSM}\) was the similarity between the interests. Suppose the agent had a series of interest \(XI_{i}\) whereas other interactive agents had interests, \(YI_{i}\). The Attention Subjective was computed by calculating the similarity between the interests of the two agents using Equation (10). \[percepts_{p}=f_{perception}(percepts_{s}) \tag{2}\]2  
**Confidence** of a chunk of personal semantic \(C_{PSM}\) indicated how many times the agent correctly thought in the same way. Confidence of personal semantics \(C_{PSM}\) was the number of similar events divided by the total events.