# From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents
## 4 Affective Agents with Practical Reasoning
### Use of Core Processes and Attributes in Computational Approaches
#### 4.2.1 Affect and Memory  
An example that addresses the relation of affect with cognition is the work proposed in [95]. In this work the authors propose LIDA, which is composed of an agent architecture (LIDA\({}_{\text{I}}\)) and agent model (LIDA\({}_{\text{C}}\)), for cognition, emotion, and learning that is inspired in cognitive science and empirically grounded in cognitive neuroscience. According to the authors, it is \"a plausible candidate for a unified theoretical framework of cognition\". Autonomous agents in LIDA\({}_{\text{I}}\) must continually sense the environment, interpret perceptions, and then act, where the actions must be appropriate to achieve the agent's goals. LIDA mainly combines action selection, motivation via emotions, an attention mechanism, and learning.  
Figure 3 shows the processes related to LIDA\({}_{\text{C}}\) (a conceptual model where the emotion components still require computational testing), which include _Perception_, _Means-end Reasoning_, _Deliberation_, _Action Selection_, _Appraisal_, and _Affective Memories_. In LIDA, both the affect-cognition and affect-reasoning relations are considered. The first relation takes place through the influence of emotion on learning where, up to a point, there is a direct correlation between the levels of arousal that are involved with current emotions and the encoding of information in memory. Beyond that point, arousal becomes a hindrance to learning. The second relation happens when the likelihood for actions to be selected depends on current emotions. In LIDA, \"schemes\" of possible actions have a context and a result as well as an activation value. Emotions increase or decrease the activation value of actions.  
In LIDA\({}_{\text{C}}\), _Affective Memories_ are stored and maintained. Moreover, memory is categorized according to the type, function, and duration of the structures learned. This categorization includes the following systems: sensory, perceptual, spatial, episodic, attentional, procedural, and sensory-motor. Specifically, in the perceptual system, a perceptual associative memory (PAM) stores \"nodes\" containing an emotion and its intensity. An _Appraisal_ process links these nodes with \"node/links\", which include the perceptual structure that represents the event that caused the emotion. Thus, this _Appraisal_ process includes an _Affect Generation_ process.  
Figure 2: Framework proposed for the representation and analysis of the affect-cognition and affect-reasoning relations (left) and possible processes and cognitions (right) that can be addressed in a computational approach that models intelligent affective agents.  
For _Perception_, there is an \"understanding phase\" in the LIDA cognitive cycle, where the representation of the agent relationship with its environment is also stored in the PAM. In addition, in an \"action and learning\" phase and as part of the _Deliberation_ process, templates (or schemes) of possible actions are instantiated with the current situation by evaluating the intersection of the context of those templates with the situation. Then, an _Action Selection_ mechanism chooses a single action from the instantiated templates. The _Means-end Reasoning_ process in LIDA is performed through procedural learning (where new actions and sequences of actions for accomplishing new tasks are learned). A procedural memory stores the resulting executing behaviors. This procedural learning corresponds to the _Option Generation_ process.  
There is not an explicit reference for the processes or cognitions that are not highlighted in Figure 3. Nevertheless, it is argued that some of them can be represented by PAM nodes and their activations.