A Knowledge Representation and Reasoning Module for

a Dialogue System in a Mobile Robot

ıs Seabra Lopes, Ant

onio J. S. Teixeira and Marcelo Quinder

IEETA, Departamento de Electr

onica e Telecomunicac¸

oes

University of Aveiro,

3810-193 Aveiro, Portugal

Abstract. The recent evolution of Carl, an intelligent mobile robot, is presented.

The paper focuses on the new knowledge representation and reasoning module,

developed to support high-level dialogue. This module supports the integration

of information coming from different interlocutors and is capable of handling

contradictory facts. The knowledge representation language is based on classical

semantic networks, but incorporates some notions from UML. Question answer-

ing is based on deductive as well as inductive inference.

1 Introduction

Due to the successive advances in robotics-related technologies, robots are closer to

humans. Intelligent service robots capable of performing useful work in close coopera-

tion/interaction with humans are the next generation of robots.

However, in order to reach that phase, it is necessary to include in their design

such basic capabilities as linguistic communication, reasoning, reactivity and learning.

“Integrated” Intelligence identiﬁes an approach to building intelligent artiﬁcial agents

in which the integration of all those aspects is considered [1].

This is the scope of CARL (Communication, Action, Reasoning and Learning in

Robotics), a research project started in our institute in 1999, in the framework of which

a robot prototype was developed, Carl [2]. The software architecture of Carl is based

on a set of Linux processes. One of them handles general perception and action, includ-

ing navigation. Other processes are dedicated to speech processing and touch screen

interaction. An animated face displays appropriate emotions. High-level reasoning and

natural language generation are mostly based on the Prolog engine. The central manager

is an event-driven system.

The human-robot communication process is modeled as the exchange of messages,

much like is done in multi-agent systems. The set of performatives or message types

in our Human-Robot Communication Language (HRCL) is inspired in KQML [3]. The

currently supported set of performatives includes tell, ask, ask

if, and achieve. The Spo-

ken Language Understanding (SLU) module combines a robust parser with memory

based learning and is capable of performing deep analysis - in grammatically correct

sentences (or mostly correct), and shallow analysis - in sentences with severe errors [4].

The current knowledge management system of Carl supports the acquisition of

knowledge obtained from interaction with human interlocutors [2]. The interaction with

Seabra Lopes L., J. S. Teixeira A. and Quinderé M. (2005).

A Knowledge Representation and Reasoning Module for a Dialogue System in a Mobile Robot.

In Proceedings of the 2nd International Workshop on Natural Language Understanding and Cognitive Science, pages 172-177

DOI: 10.5220/0002573901720177

 SciTePress

Carl is viewed as a sequence of interaction sessions. All that is done is to associate the

collected information to the respective interaction session. The current system does

not perform any kind of information integration and, in particular, does not attempt to

resolve contradictions between pieces of information obtained from the different inter-

locutors.

The latest developments of Carl are focused on the knowledge representation and

reasoning (KRR) module, designed to increase the actual dialogue capabilities of the

robot. This new module implements a semantic network and is capable of integrating

information obtained from different interlocutors, even if they are contradictory. When

a question is directed to Carl, inference mechanisms process all the acquired data to

give the best response.

Although there are various works on knowledge representation (KR) for robotics,

few of them are focused on supporting the dialogue system. Skubic et al developed a

framework to use spatial language in human-robot dialogues [5]. However, they are not

focused on acquiring information from different interlocutors and do not consider the

acquisition of non-spatial knowledge.

Kamp and Reyle proposed the Discourse Representation Theory (DRT), which -

like the other theories of dynamic semantics - accounts for the context dependence of

meaning. DRT uses Discourse Representation Structures (DRS) for semantic represen-

tation and a model-theoretic interpretation of those DRSs. The new version of DRT uses

a bottom-up approach in the construction of the representations instead of the top-down

approach of the original version [6].

Benferhat et al presented strategies for conﬂict resolution developed to deal with ex-

ception handling and iterated belief revision, but which could also be applied in merging

information from different sources [7]. Their work consists on weakening the contra-

dictory data, rather than eliminating them completely.

Brazdil and Torgo have developed a method to construct an integrated knowledge

base from several separated ones [8]. In their method, the knowledge bases used as input

can either be obtained by querying an expert or on the basis of data, using inductive

learning tools.

In this work, all the acquired data is kept on the knowledge base, and the respon-

sibility of resolving contradictions is left to the inference mechanisms. Besides that, it

supports a natural language dialogue system and it was designed to be applied in an

intelligent mobile robot.

The paper is structured as follows. Section 2 describes the KR language. The infer-

ence mechanisms are explained on section 3. The KRR module is presented on section 4

and section 5 concludes the paper with reference to future work.

2 Knowledge Representation Language

Complex domains, like the one of a dialogue system in an intelligent mobile robot,

require a general and ﬂexible KR [9].

The scenario in which the robot must act involves conversation with various inter-

locutors and exchange of information with them. The module should provide function-

172

alities for knowledge acquisition and question answering. It’s important to note that the

agent should be able to start with no prior knowledge.

Since the robot can acquire data from different sources (interlocutors), the KRR

module must handle contradictory pieces of information.

Semantic networks [10] address the main representation requirements to support

a high level dialogue. It is very easy to represent the entities with them. Inference is

simple, all one has to do is to follow the relations between entities. Semantic networks,

not only provide ways for efﬁcient computing, they also provide a very intelligible

layout.

Using semantic networks we can easily apply the rule of inheritance, in which all

properties of the supertype are copied to the subtypes, except if there is a redeﬁnition

of the property in the subtype.

We have based the deﬁnition of our KR language on typical deﬁnitions of semantic

network and on class and object diagrams of UML [11].

We have used prolog predicates for this, including the declaration of types, objects

(instances of types) and various relationships between them. This language assumes

that objects are identiﬁed by system-generated identiﬁers. In the implementation, when

a new object comes up, a new integer identiﬁer is generated. The predicates that provide

information on speciﬁc objects include instance(ObjID, Type), name(ObjID, Name) and

attribute(ObjID, Attribute, Value). Generalization is declared with subtype/2. Standard

UML relations (aggregation, composition, association) are also supported.

3 Basic Inference Mechanisms

There are three main types of inference: deduction, induction and analogy [12]. Take

the logical entailment (1), in which P represents the premise, BK the background

knowledge and C the consequence.

P ∪ BK |= C . (1)

Deduction is truth preserving. It derives consequence C from a given premise P

and background knowledge BK. Here, this kind of inference is used when the type has

an attribute with a default value and a question is made about the object’s attribute.

Consider the Fig. 1 and suppose somebody asks “how many legs does John have?”.

Since there is no information about legs in the object John, the module uses deduction

to get answer “2” from the type Human.

Human

John

Legs

instance

Legs:2

Fig.1. Deduction

173

If there is BK and C, induction can be used to hypothesize a premise P. In this work,

inductive inference is used when the objects of a type have some attribute information

that the type itself does not have. Take the representation in Fig. 2. If the question “what

does a cat like?” is made, induction is used to obtain the answer “milk” from the objects

of the type Cat, since this information is not directly asserted in the type itself.

Cat

Tom

instance

Jim

instance

Milk

Likes

: Milk

Fig.2. Induction

Finally, analogy is a combination of deduction and induction. Considering the knowl-

edge given in Fig. 3, suppose someone asks “what does Bob like?”. Since this informa-

tion is given neither on the object Bob itself nor on the type Cat, the module has to: ﬁrst

use induction from the objects Tom and Jim to the type Cat; then use deduction from

the type Cat to the object Bob.

Cat

Tom

instance

Jim

instance

Milk

Likes

Bob

instance

Likes

: Milk

Likes : Milk

Fig.3. Analogy

4 The Reasoning System

The functionalities of the KRR module are provided mainly by the procedure tell(Int,

Fact) – the interlocutor Int tells Fact to the system; and ask(Fact, Conf) – the system is

asked about Fact, the conﬁdence in the answer, Conf, is returned.

The procedure tell simply stores the information given by the interlocutors. Infer-

ence is used in ask in order to provide the most suitable answer.

Conﬁdences are calculated as follows. Consider a property of an object or type for

which a certain value is supported by N interlocutors and that a total of T interlocutors

provided values for this property. In this case, the conﬁdence that the mentioned value

is the correct one is given by:

Conf (N, T ) =

∗



1 −

T + 1



. (2)

174

Note that the conﬁdence of answers based on few statements is reduced.

If the question is about an attribute value in a type, Algorithm 1 is used to determine

the value. The tree traversal step of the algorithm computes the frequency of occurrence

of the possible values of the Attribute in the type and all its subtypes (and sub-subtypes,

etc) and respective objects. If there is a supertype, the result of the tree traversal step is

combined with a similar result inherited from the supertype.

Algorithm 1: GET VALUE

input : Type, Attribute

output: ((V

, C

), .., (V

, C

)) in which V stands for Value, C for conﬁdence and k the

number of different values of Attribute

begin

, .., V

←− possible values of Attribute

In a tree traversal, compute the frequencies of occurrence, N

, .., N

, of each

possible value of Attribute in Type and all its subtypes (and sub-subtypes, etc) and

respective objects

T ←

i=1

(total number of statements about values of Attribute)

for i ← 1 to k do

←− conf (N

, T ) (According to Equation 2)

if Type has no supertype then

return ((V

, C

), .., (V

, C

))

ST ←− supertype of Type

((V

, C

′

), .., (V

, C

′

)) ←− GET

VALUE(ST, Attribute)

for i ← 1 to k do

′′

←− (C

+ C

′

)/2

return ((V

, C

′′

), .., (V

, C

′′

))

end

If the question is about an attribute value in an object, conﬁdences for all possible

values are computed in the object (equation 2) and in the type (Algorithm 1). The value

with the highest combined conﬁdence is returned.

Although deduction is truth preserving, type information provided by interlocutors

is not necessarily more reliable than object information, so deductive inference does not

have a stronger weight than inductive inference.

The implementation of this module, as described, was done in Prolog.

5 Conclusion and Future Work

In this paper, the recent evolution of the intelligent mobile robot Carl was presented.

The paper focuses on the new KRR module. The KR language is based on semantic

networks and incorporates some notions from UML. The reasoning system combines

deductive and inductive inference.

175

Some important aspects have not yet been addressed. One of them is induction on

attributes with continuous values. Another problem is that inheritance does not take

into account the conﬁdence on the generalization links (subtype). A third problem is

that when evaluating the values of an attribute it is assumed that only one is correct.

This is a limitation since, in general, an object or type can have several simultaneously

correct values for an attribute or relation.

Besides these limitations, we also plan to allow differentiating the interlocutors.

It would be reasonable to give more value to the information coming from someone

trusted by the robot. We are considering the development of a heuristic to give weight

to the interlocutors in order to have more truthful answers, especially when they involve

contradictory facts.

Current work addresses the development of a module to extract the same semantics

used by the KRR module from the sentences recognized.

References

1. Seabra Lopes, L. and Connell, J.H. (eds.): Semisentient Robots: special issue of IEEE Intelli-

gent Systems. vol. 16, n. 5. Computer Society (2001) 10-14.

2. Seabra Lopes, L.: Carl: from Situated Activity to Language Level Interaction and Learning.

Proc. IEEE Int’l Conf. on IROS, Lausanne, Switzerland (2002) 890-896

3. Labrou, Y. and Finin, T.: A Proposal for a New KQML Speciﬁcation. Technical Report CS-

97-03, University of Maryland at Baltimore County, (1997).

4. Rodrigues, M., L. Seabra Lopes, and A. Teixeira, An Hybrid Approach for Spoken Natural

Language Understanding Applied to a Mobile Intelligent Robot. Proc. Natural Language Un-

derstanding and Cognitive Science (NLUCS), (2004) 145-150

5. Skubic, M., D. Perzanowski, et al.: Spatial Language for Human-Robot Dialogs. IEEE Trans-

actions on Systems, Man, and Cybernetics, C 34(2): (2004) 154-167

6. Kamp, Hans, J. van Genabith, U. Reyle Discourse Representation Theory. Chapter in: Hand-

book of Philosophical Logic (2005), D. Gabbay and F. Guenthner (eds.)

7. Benferhat, Salem, et al.: Weakening conﬂicting information for iterated revision and knowl-

edge integration. Artif. Intell, 153(1-2), (2004) 339–371

8. Brazdil, P. and L. Torgo: Knowledge Acquisition via Knowledge Integration. Current Trends

in AI, B. Wielenga et al.(eds.), (1990) IOS Press, Amsterdam.

9. Russell, Stuart and Peter Norvig Artiﬁcial Intelligence: A Modern Approach. Prentice Hall,

1st edition (1995); 2nd edition (2003).

10. Sowa, John F.: Semantic Networks. MIT Encyclopedia of Cognitive Science,

http://www.jfsowa.com/pubs/semnet.htm

11. Rational Software Corporation: UML Semantics, v1.0. Santa Clara, CA (1997).

12. Michalski, R. S.: Inferential Theory of Learning: Developing Foundations for Multistrategy

Learning. Machine Learning: A Multistrategy Approach, 1994

176