New Paradigm of Context based Programming-Learning

of Intelligent Agent

Andrey V. Gavrilov

Novosibirsk State Technical University, Kark Marx av., 20, 630092, Russia

Abstract. In this paper we propose new paradigm combining concepts “pro-

gramming”, “learning” and “context” for usage in development of control sys-

tem of mobile robots and other intelligent agents in smart environment. And we

suggest architecture of robot’s control system based on context and learning

with natural language dialog. The core of this architecture is associative memo-

ry for cross-modal learning. Main features and requirements for associative

memory and dialog subsystem are formulated.

1 Introduction

One of challenges in development of intelligent robots and other intelligent agents is

human-robot interface. Two kinds of such known interfaces are oriented on pro-

gramming and learning respectively. Programming is used usually for industrial ro-

bots and other technological equipment. Learning is more oriented for service and toy

robotics.

There are many different programming languages for different kinds of intelligent

equipment, for industrial robots-manipulators, mobile robots, technological equip-

ment [1], [2]. The re-programming of robotic systems is still a difficult, costly, and

time consuming operation. In order to increase flexibility, a common approach is to

consider the work-cell programming at a high level of abstraction, which enables a

description of the sequence of actions at a task-level. A task-level programming envi-

ronment provides mechanisms to automatically convert high-level task specification

into low level code. Task-level programming languages may be procedure oriented

[3] and declarative oriented [4], [5], [6], [7], [8] and now we have a tendency to focus

on second kind of languages. But in current time basically all programming languag-

es for manufacturing are deterministic and not oriented on usage of learning and

fuzzy concepts like in service or military robotics. But it is possible to expect in fu-

ture reduction of this gap between manufacturing and service robotics.

On the other hand in service and especially domestic robotics most users are naive

about computer language and thus cannot personalize robots using standard pro-

gramming methods. So at last time robot-human interface tends to usage of natural

language [9], [10] and, in particular, spoken language [11], [12]. The mobile robot for

example must understand such phrases as “Bring me cup a of tea”, “Close the door”,

“Switch on the light”, “Where is my favorite book? Give it to me”, “When must I

Gavrilov A. (2009).

New Paradigm of Context based Programming-Learning of Intelligent Agent.

In Proceedings of the International Workshop on Networked embedded and control system technologies: European and Russian R&D cooperation,

pages 94-99

 SciTePress

take my medication?”.

Using natural language dialog with mobile robot we have to link words and

phrases with process and results of perception of robot by neural networks. In [13] to

solve this problem the extension of robot programming language by introducing of

corresponding operators was proposed. But it seams that such approach is not enough

perspective.

In this paper we suggest novel bio- and psychology-inspired approach combining

programming and learning with perception of robot based on usage of neural net-

works and context as result of recognition and concepts obtained by learning during

dialog in natural language. In this approach we do not distinguish learning and pro-

gramming and combine: a declarative (description of context) and procedural know-

ledge (routines for processing of context implementing elementary behavior) on the

one hand, learning in neural networks and ordering of behavior by dialog in natural

language on the other hand.

2 Proposed Paradigm and Architecture of Intelligent Agent

Our paradigm is based on relationships with other known paradigms and is shown in

Fig.1.

Fig. 1. Relationships between paradigm “Context based Programming-Learning” and other

paradigms.

Concepts are associations between images (visual and others) and phrases (words) of

natural language. In simple case we will name as concept just name of them (phras-

es). These phrases are using for determination of context in which robot is perceiving

environment (in particular, natural language during dialog) and planning actions. We

will name them as context variables.

Programming

of Equipment

and Robots

Deliberative

paradigm in

Robotics

Context

based Pro-

gramming-

Learning

Reactive

paradigm in

Robotics

Natural

Language

Processing

Cognitive

robotics

Neural

Networks in

Robotics

Fig. 2. Intelligent agent functional structure oriented on context based programming-learning.

Context is a tree of concepts (context variables) and is recognizing by sensor in-

formation and determining by dialog with user. Feedbacks between concepts/context

and recognition of images/phrases mean that recognition is controlled by already

recognized things.

Dialog with user aims to describe elementary behaviors and conditions for its start-

ing. To start any behavior the system recognizes corresponding concept-releaser as in

reactive paradigm of control system of robot.

Behavior is not influence on just actions but also on context. Moreover, behavior

may be not connecting directly with actions in environment. In this case we have only

thinking under context variables. And even when behavior is oriented on execution of

actions it is possible to block this connection and in this case we have any simulation

in mind sequence of actions (e.g., it may be mean as planning).

The associative memory must satisfy to follow requirements:

1) to allow usage of both analog and binary inputs/outputs,

2) to provide incremental learning,

3) to provide storing of chain of concepts (as behavior or scenarios).

The elementary behavior is similar to subroutine and contains sequence of actions

adaptable to context variables which may be viewed as parameters of subroutine. Of

cause, we need to use some primitives directly connecting with elementary actions

and describing parameters of these actions or basic context variables. Connections

between these primitives and words of natural language must be a prior knowledge of

robot obtaining at development of robot or during a preliminary teaching by special-

ist. It may be simple language similar to CBLR, proposed by author for context based

programming of industrial robots in [9], [14]. Feature of this language is absence of

Current context

Recognition (understanding)

of natural language

Recognition of images

Sequence of actions

Concepts

Behaviors

Actions

Speech or text

Releaser

Description

Associative memory

Images

different motion primitives. There we have just one motion primitive. All other primi-

tives aims to represent of context variables needed for execution of this motion primi-

tive. To distinguish these primitives and usually using motion or geometric primitives

we will call its below as output primitives.

3 Cross-Modal Incremental Learning and Associative Memory

Associative memory satisfied to above requirements may be based on hybrid ap-

proach and similar to Long-Short Term Memory (LSTM) [15] or table based memory

proposed by author in [16].

Concept in Fig. 2 is more common thing as context. Concepts are introducing by

dialog subsystem for determination of objects, events, properties and abstraction. But

context consists of several concepts separated by three rules: 1) preliminary defined

concepts (names) are using as names of “parameters” to utilize in elementary beha-

vior (see 4); 2) some concepts are using as values of these “parameters”; 3) some

concepts (releasers) are using as names of any events causing any behavior.

Dialog subsystem must provide robustness to faults in sentences. For it implemen-

tation may be used approach proposed by author in [17] and based on semantic net-

works combining with neural networks algorithms. This approach is oriented on

fuzzy recognition of semantics. Dialog subsystem must provide attend the visual

recognition subsystem to link word (phrase) with recognizing image. In this case the

recognition subsystem create new cluster with center as current feature vector. And

couple of this feature vector and word (phrase) is storing in associative memory for

concepts. It may be as result of processing of follow sentence “This is table”. In con-

trast to that case when system processes sentence “Table is place for dinner” the new

cluster is not created and associative memory is used for storing of association be-

tween words (phrases).

Thus associative memory must be able to store associations between both couple

of symbolic information and couple of word (phrase) and feature vector. Besides

concept storing in associative memory must have some tags: basic concept (prelimi-

nary defined) or no, name or value, current context or no. And every concept in asso-

ciative memory must be able to link in chain with other ones. The order of concepts

in chain may be defined by dialog subsystem.

4 Output Primitives for Mobile Robot

Set of output primitives may be selected by different way. One approach to that is

determination of enough complex behaviors, such as “Find determined object”, “Go

to determined place” and so on. These actions may be named as motion primitives.

Determined object and place must be obtained from context as value of correspond-

ing context variable. And these variables may be viewed as another kind of output

primitives: context primitives. In this case such actions must include inside any strong

intelligence and ones limit capabilities to learn mobile robot. Another approach may

be based on very simple motion primitives such as “act”, which may be just one. If we

want to make capability to say anything by robot not only during dialog inside dialog

subsystem we need introduce also at least one motion primitive “say”. All another

output primitives are context primitives and ones define features of execution of pri-

mitive “act”. In other words ones are parameters of subroutine “act”. Examples of

robot’s context variables are shown in Table 1

Table 1. Examples of context primitives.

Name of context

primitive

Possible value

How this parameter influence

on execution of motion primi-

tive

Object Name of object May be used in action “say”

Internal state Good, Bad, Normal May cause motion to or from

Object

Direction Left, Right, Forward, Back May cause corresponding turn

depending on Internal state

Person Name of person May be used in action “say”

Obstacle distance Far, Middle, Close May be used in “act”

Obstacle type Static, Dynamic May be used in “act”

Speed Low, Normal, High May be used in “act”

5 Conclusions

In this paper we have suggested new paradigm combining programming and learning

of mobile robot based on usage of context and dialog in natural language. Architec-

ture of programming/learning of mobile robot was proposed. Some features of asso-

ciative memory and dialog subsystem were formulated. Now this architecture is de-

veloping for simulated mobile robot and then to be implemented in real robot. This

approach may be used for programming/learning of smart object in ambient intelli-

gence [18].

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