=Paper=
{{Paper
|id=None
|storemode=property
|title=Unified Perception-Prediction Model for Cognitive Agents
|pdfUrl=https://ceur-ws.org/Vol-911/13_LANMR12.pdf
|volume=Vol-911
|dblpUrl=https://dblp.org/rec/conf/lanmr/ArzolaZ12
}}
==Unified Perception-Prediction Model for Cognitive Agents==
https://ceur-ws.org/Vol-911/13_LANMR12.pdf
Unified Perception-Prediction Model for
Cognitive Agents
Sergio Arzola1 and Claudia Zepeda1
Benemérita Universidad Autónoma de Puebla
Facultad de Ciencias de la Computación
sinrotulos@gmail.com, czepedac@gmail.com
Abstract. 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 program-
ming. The Unified Perception-Prediction Model is based on how the brain
works according to neuroscience research.
Key words Cognitive science, Unified perception-prediction model, ar-
tificial intelligence, logic programming, stable semantics.
1 Introduction
The research about how the brain works has increased over the last years. Neu-
roscience 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 [7]. Though
this model has been used for Context Aware Text Recognition, we will use this
model in order to model intelligent agents. The main purpose of using the UPP
Model as reference is to create a better understanding of the physical world for
the agent for acting properly. With this model the agent can be capable of make
inferences of the model it has of the world with perceptions and predictions. In
addition, we present a logic approach of this model for intelligent agents using
the stable semantics [4].
In [3] it is remarked the importance of perception in the brain as well as pre-
diction in order to interact within the physical world. There exists an infinite
loop of interaction between perception and prediction because the brain can not
perceive what it does not expect to perceive as well as the brain can not predict
what it does not have information about [3].
2 Background
In this section we summarize some basic concepts and definitions used to under-
stand this paper.
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2.1 Logic programs
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 Stable semantics
From now on, we assume that the reader is familiar with the notion of classical
minimal model [5]. We give the definitions of the stable semantics for normal
programs.
Definition 1. [6] Let P be a normal program and let M ⊆ LP . Let us put
P M = P OS(RED(P, M )), then we say that M is a stable model of P if M is
a minimal classical model of P M .
3 The Unified Perception-Prediction Model for Cognitive
Agents
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 [3]. However, for
agent programming we consider the following assumptions:
The World’s Model must have a background knowledge of the problem, which
is intended to solve.
Perceptions must be in the same terms in which data is stored.
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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 [3], which is located in the world’s
model. Thus, the process start from the inside, the model brings predictions
based on the world’s model, in this way the agent can predict what perceptions
the agent should be receiving. These perceptions are compared with the signals
sensed in order to compare them, if the signals are equal, a reinforcement is
made, otherwise, it indicates that some errors have occurred and the world’s
model must be updated. These errors bring the agent a better understanding
of the world by a trial and error technique. Thus, the agent develops a better
world’s model.
4 Example
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 program-
ming 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
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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).
Inference:
emits(b,arbitrary).
As we can see, the model is useful for agents. Furthermore, logic programming
makes this task more easily.
5 Conclusions and Future Work
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 [2] [1] however, there should be more research between these
areas.
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.
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