In this work, we adapt the unified perception-prediction model for cognitive agents in order to solve related perception and prediction problems. Furthermore, we present an approach based in logic programming. The Unified Perception-Prediction Model is based on how the brain works according to neuroscience research.
The research about how the brain works has increased over the last years.
Neuroscience started to provide mechanistic biological oriented explanation for every
aspect of behavior, such as psychology, economics, anthropology and so on. In
particular, we will focus on perception and prediction studies at an abstract
level in order to explain the Unified Perception-Prediction Model [
In [
A signature L is a finite set of elements that we call atoms, or propositional symbols. The language of a propositional logic has an alphabet consisting of proposition symbols: p0, p1, . . . ; connectives: ∧, ∨, ←, ¬; and auxiliary symbols: (, ). Where ∧, ∨, ← are 2-place connectives and ¬ is a 1-place connective. Formulas are built up as usual in logic. A literal is either an atom a, called positive literal ; or the negation of an atom ¬a, called negative literal. The formula F ≡ G is an abbreviation for (F ← G) ∧ (G ← F ). A clause is a formula of the form H ← B (also written as B → H), where H and B, arbitrary formulas in principle, are known as the head and body of the clause respectively. The body of a clause could be empty, in which case the clause is known as a fact and can be denoted just by: H ←. In the case when the head of a clause is empty, the clause is called a constraint and is denoted by: ← B. A normal clause is a clause of the form H ← B+ ∪ ¬B− where H consists of one atom, B+ is a conjunction of atoms b1 ∧ b2 ∧ . . . ∧ bn, and ¬B− is a conjunction of negated atoms ¬bn+1 ∧ ¬bn+2 ∧ . . . ∧ ¬bm. B+, and B− could be empty sets of atoms. A finite set of normal clauses P is a normal program.
Finally, we define RED(P, M ) = {H ← B+, ¬(B− ∩ M ) | H ← B+, ¬B− ∈ P }. For any program P , the positive part of P , denoted by P OS(P ) is the program consisting exclusively of those rules in P that do not have negated literals. 2.2
From now on, we assume that the reader is familiar with the notion of classical
minimal model [
Definition 1. [
In this section we establish a cognitive agent model based on the UPP Model.
This model consists in three main parts: the Perception part, the Prediction part, and World’s Model.
The way that this model works is analogous as the brain works [
Perceptions must be in the same terms in which data is stored. Predictions must be made based on the previous knowledge the agent has or the current perceptions.
The Perception part receives signals of the world through its sensors. The Prediction part makes inferences from information stored in the Worlds Model part or Perception. Both parts store the information in the Worlds Model part, which is always being updated.
It is easy to see the loop between Perception and Prediction as well as the World’s Model as a storage of both parts.
Perceptions depend upon a prior belief [
In this section we present an example in order to show how this model works.
This example illustrates how inferences of the world can be done through perception as well as the previous knowledge of it.
Example 1. Imagine the following scenario: There are two agents in the same building but, they are in different rooms. The agents can perceive arbitrary sounds and know that a sound can be produced by another agent.
Suppose that the agent A perceives in time 1 an arbitrary sound. Then, the agent A by this perception, can infer that agent B may produce this sound.
However, this inference may be wrong because it is not explicitly said that agent B makes the sound. But the agent A can valid this inference by asking the agent B.
We can model this problem as well as agent A inference into logic programming clauses. The first part is about the previous knowledge the agent has, which corresponds to the scenario. This information corresponds to the part of the World’s Model of the Unified Perception-Prediction Model. The second part is about the perceptions the agent A has. This is according to perception part of the Unified Perception-Prediction Model. The third part is about the inference the agent A makes with the previous knowledge and perception. This inference should be stored into the world’s model.
Knowledge: agent(a). agent(b). sound(arbitrary). emits(X,Y) ← agent(X), sound(Y).
Perception: perception(P, T) ← sound(P), time(T). perception(arbitrary,1).
As we can see, the model is useful for agents. Furthermore, logic programming makes this task more easily. 5
Here we introduced a new model for intelligent agents, which is based upon
cognitive science. There are few works which involve logic programming and
cognitive science [
This model pretends to open a new framework for logic programs and to solve problems where the agent needs to perceive or predict the environment in order to act.
There is a lot of work ahead, for creating perception or prediction rules, as well as, create an implementation framework of this model.