# From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents
## 4 Affective Agents with Practical Reasoning
### Proposal of a Conceptual System for Intelligent Affective Agents
#### 4.1.1 Cognitive Processes of Intelligent Agents  
According to [93], general architectures for intelligent agents can be classified into four classes of agents: _logic-based_ (decisions are taken through logical deduction), _reactive_ (decisions respond to direct mappings of situations to actions), _belief-desire-intention_ (beliefs, desires, and intentions are manipulated in order to make decisions), and _layered_ (where different software layers, which reason about the environment at different levels of explicitness and abstraction, determine decisions to make). Specifically, BDI (belief-desire-intention) architectures are grounded on the philosophical roots of _practical reasoning_, which is the process whereby, at each moment, an agent decides the actions to perform to reach its goals [94]. The BDI architecture is well suited for building computational models of affective agents. Some of the arguments that support this include that it has its foundation in philosophical and psychological theories, it contains the main components of emotions (which are beliefs and desires), and it can be enhanced (along with its logic), extended, and/or combined to account for new structures or processes [90].  
Practical reasoning involves mainly two processes: _deliberation_ (where the agent decides what to do by determining the goals to be attained) and _means-end_ reasoning (which involves how to achieve these goals). Most BDI agents perform these processes through four more specific processes which are: _belief revision_ (which includes _perception_ and determines new _beliefs_ on the base of percepts and current beliefs); _option generation_ (which determines the agent _desires_, on the basis of its _beliefs_ and current _intentions_); _filter_ (which determines new _intentions_ on the basis of current _beliefs_, _desires_, and _intentions_); and _action selection_ (which determines the action to perform by considering current _intentions_).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Relations Emotion–Cognition** & **Main Processes** & **Cognitions Involved** \\
**Emotion–Behavior** & & \\ \hline Emotions and memory & Storing of emotional memories & Emotional memories (emotional events) \\ \hline Influence of emotions on behavior & Generation of reactive behavior & Intentions, Past experiences, Emotional memories, Culturally shared evaluations, \\ \hline Emotions and their evolutionary account & Bodily changes, generation of facial expressions, body gestures, etc. & Expectations, Affective state \\ \hline Appraisal & Perception Affect generation & Percepts, Beliefs, Probability of beliefs (herein Belief probability), Concerns, Desires, Norms, Capabilities, Resources, Social rules, Affective state \\ \hline Regulation of emotions (herein Emotion regulation) & Generation of coping behavior & Beliefs, Desires, Intentions, Expectations \\ \hline Evaluative implications of emotion & Perception & Percepts, Beliefs content and strength \\ \hline Emotions and social interactions & Affect generation (through empathy, emotional contagion, or social learning) & Beliefs, Affective state \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main processes and cognitions of emotion–cognition and emotion–behavior theories.  
The _option generation_ process decides how to achieve intentions, and, hence, is the one responsible for the agent's _means-end_ reasoning. On the other hand, _deliberation_ is performed by the _filter_ process. This process which not only adopts new intentions in order to facilitate existing ones or to discover new opportunities, it can also drop intentions (intentions that are either no longer achievable or that diminish the agent's expected gains) that are not workable and can retain intentions that have not yet been achieved [93].