INTEGRATING AGENTS WITH CONNECTIONIST SYSTEMS

TO EXTRACT NEGOTIATION ROUTINES

Marisa Masvoula, Panagiotis Kanellis and Drakoulis Martakos

Department of Informatics and Telecommunications, Nationaland Kapodistrian University of Athens

University Campus, Athens15771, Greece

Keywords: Evolving Connectionist Systems, Negotiation Routines.

Abstract: Routinization is a technique of knowledge exploitation based on the repetition of acts. When applied to

negotiations it results the substitution of parts or even whole processes, disembarrassing negotiators from

significant deliberation and decision making effort. Although it has an important impact on negotiators, the

risk of establishing ineffective routines is evident. In our paper we discuss weaknesses and limitations and

we propose a generic framework to address them. We consider routines as evolving processes and we take

two orientations. The first concerns a communicative dimension to allow for external evaluation of the

applied routines and the second concerns enforcement of the system core with evolving structure that

adjusts to routine changes and flexibly incorporates new knowledge.

1 INTRODUCTION

Organizations mostly engage in improvement

strategies that concern knowledge exploitation and

exploration. Exploitation is considered similar to

recalling past experience to deal with current issues,

while exploration is considered similar to adopting

imitative or innovative tactics in the search for new

knowledge. Several techniques have been developed

to support exploitation and involve knowledge

refinement, routinization and elaboration of existing

ideas, paradigms, technologies, processes, strategies

and knowledge. Exploration, on the other hand, is

mostly supported through experimentation with new

ideas, paradigms, technologies, processes, strategies

and knowledge that improve on old ones. As

depicted in (Dyba, 2000) the basic balance problem

is “to undertake enough exploitation to ensure short

term results and concurrency to engage in

exploration to ensure long term survival”. Our study

mainly focuses on routinization as a means of

knowledge exploitation and its employment in

negotiations. Negotiation routines are understood as

repetitive acts in several stages of the negotiation

process. The identification of the context that

enables routine development and routine

‘subconscious’ retrieval, result to the development

of more effective negotiators who accomplish the

same outcomes with the minimum deliberation

effort. Nevertheless several limitations have been

acknowledged. Efficiency decrease that arises from

inflexibility and rigidity produced by routine

establishment, as well as lack of formal theoretical

frameworks and theories to routine application in

negotiations are among the most important

impediments. Furthermore routines should be

viewed as evolving processes, since the repetitive

acts may change over time even under similar

negotiation contexts, and distinct AI techniques are

not adequate to support such processes. In sections 2

and 3 we give a brief definition and background of

routines and their development in several

negotiation stages, as well as an analysis of

theoretical weaknesses and shortcomings of routine

establishment. In section 4 we identify the features

that a system acquiring and extracting negotiation

routines should be enhanced with, and we propose a

generic framework for such a purpose. In section 5

we explain how the proposal is expected to address

the discussed limitations and appose future research

issues.

251

Masvoula M., Kanellis P. and Martakos D. (2009).

INTEGRATING AGENTS WITH CONNECTIONIST SYSTEMS TO EXTRACT NEGOTIATION ROUTINES.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

251-256

DOI: 10.5220/0002155302510256

 SciTePress

2 ROUTINES FOR KNOWLEDGE

EXPLOITATION

“Routines” is the general term used for the definition

of all regular and predictable behavioral patterns

(Nelson, Winter, 1982). Although there is no final

agreement on an exact definition, researchers indent

that repetition of acts is a necessary precondition for

the acquisition and application of routines (Kesting,

2007). When an agent executes an act for the first

time, it faces the challenge of identifying and

evaluating alternatives that will lead to an intended

state. It is given the opportunity to observe the

results of the initial solution and if it is effective, it

can be applied to similar problems. The substance of

routines is the specific knowledge acquired by the

repeated planning and execution of an act combined

with the ability to apply this knowledge to specific

situations. It has the potential to substitute deliberate

planning and decision making since it is used to

determine what operations to conduct in order to

realize certain intended state. Routinization forces

agents to develop ‘best practices’.

2.1 Development of Routines in

Negotiation

Negotiation is most commonly defined as the search

for an agreement that satisfies the requirements of

two or more parties. Several scientific fields have

made contributions to the development of

negotiation theory. (Gulliver 1979, Robinson &

Volkov 1998, Putnam & Roloff 1992) discuss

negotiation models that follow normative,

prescriptive or descriptive approaches derived from

the application of economic theories, management

and social sciences respectively. Although

theoretical perspectives and definitions may vary,

there is a general consensus regarding the number of

stages in the negotiation process. Most approaches

model negotiation in three basic stages: (a)

prenegotiation stage basically concerned with

strategy formulation, commitment to rules,

observation of the other party behavior, definition of

issues and problem formulation (b) negotiation

dance, which is mainly concerned with the exchange

of offers and counter-offers and (c) execution of

negotiation results. Negotiations may be fully or

semi-automated processes and systems that facilitate

the decision making in several stages have been

developed. Nevertheless, the application of routines

in negotiation has not been extensively studied

(Kesting, Smolinski, 2007). In what context are

similar negotiation cases identified? How can

negotiation data (about products, shipping info,

particulars of buyers and sellers, orders and

dialogues of negotiation) be used to extract

knowledge which will be reused in further

negotiations? (Kesting, Smolinski, 2007) Introduces

a theoretical framework which identifies different

negotiation situations where routine can develop,

based on similarity and stability of negotiation

elements measured in two dimensions. The problem-

solving (substance of negotiation) dimension and the

communication dimension, concerning negotiation

partners. The framework in (Kesting, Smolinski,

2007) concludes that negotiation success is based on

a combination of problem-solving/analytic and

negotiation/communication skills that negotiators

possess, with routine application. Routine has an

important impact on negotiations, since it allows

negotiators to develop their capabilities, improve

their efficiency and save scarce planning capacities

and time by employing automated procedures.

3 PROBLEM STATEMENT

A commonly stated risk posed by routinization is the

application of ineffective acts. As stated in (Nelson

& Winter 1982, Winter 1986, Cohendet & Llerena

2003) with increasing repetitions, decision making

prior to the operation tends to decrease. The use of

routines entails rigidity and once a solution is

established, it is not further questioned. As a result,

formal routines alone are inadequate and might

demand improvisational skills. (Kesting, 2007)

Investigates the mechanisms that tend to make

routines resistant to change and are the major source

for inflexibility. The first concerns stability of

intention. Routines are developed to bring about a

specific intended state and as a consequence

intention defines a narrow frame that restricts the

extent of possible changes. The second mechanism

is derived from repetition which is prerequisite for

the application of routines. Repetition transcends

planning efforts, therefore routines are stable and do

not change. The third mechanism that is recognized

concerns automation. With increasing repetitions the

course of an act is automated, without further

deliberation.

The application of routines in negotiation has not

been researched, and the lack of formal models

describing the context and conditions that favor their

development is an obvious impediment. How the

negotiation environment is formally represented and

how is similarity of distinct contexts measured? To

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overcome this theoretical limitation we make the

generic assumption that negotiation context can be

represented as a vector in n

dimensional space

(input space X), and similarity of distinct contexts is

calculated through distance vectors. Furthermore, we

assume that routines can be represented as vectors in

dimensional space (output space Y) and any

framework that results to routine extraction must

provide mappings from the input space X to the

output space Y.

Another important issue we need to address

concerns the decision of the most appropriate

technique that will provide mappings from input to

output space, as long as the development of a model

with a dynamic structure in order to accommodate

new knowledge and produce new routines in a

flexible and adaptive manner. Routinization must be

viewed as an evolving process, since the negotiation

context may change over time resulting to different

outcomes. A necessary first step is the study of

existing systems that exploit previous knowledge in

order to provide advising services during the various

phases of the negotiation process. A categorization

of such systems is provided in (Braun, Brzostowski

et al., 2005) and several AI methods and techniques

have been successfully developed and used.

Nevertheless a number of problems, which are

discussed in (Kasabov, 2007), arise from the

application of such techniques to evolving

processes:

1. “Difficulty in preselecting the system’s

architecture: Computational Intelligence models

usually retain a fixed architecture (e.g. number of

neurons and connections)”. This restricts us to a

view of a close problem space (fixed input and

output space), which is not the case if we

consider that negotiation environment and

routines may change over time.

2. “Catastrophic forgetting: Systems usually forget

a significant amount of old knowledge while

learning from new data.” This fact prohibits the

development of a framework that balances

knowledge exploitation with exploration.

3. “Excessive training time required: Training

usually requires many iterations of data

propagation through an ANN structure.” If

knowledge insertion and routine extraction is

time consuming the model is not considered

efficient.

4. “Lack of knowledge representation facilities:

Existing Computational Intelligence

architectures capture statistical parameters during

training, but do not facilitate the extraction of

evolving rules in terms of linguistically

meaningful information.” If knowledge is

represented in terms of IF-THEN rules (eg. If

<context> then <routine>) in order to be

linguistically meaningful, we can consider the

exchange of locutions between agents in terms of

exchange of experience.

4 PROPOSAL

The main problem posed by routinization is the risk

of acquiring and applying ineffective routines. To

this extent, we trust that the system producing

routines should be enhanced with learning

capabilities in order to evolve according to possible

satisfaction or dissatisfaction measures caused by

the applied routines. Furthermore it should be

enhanced with communicative abilities in order to

interact with the agents that apply the routines.

The underlying framework should combine

knowledge exploitation and knowledge exploration

techniques, and should have an evolving structure in

order to adapt to environmental changes. We outline

several desirable characteristics of an evolving

model that exploits and explores negotiation

knowledge.

1. Previous knowledge can be modeled in pairs of

data (x,y), where the desired output vector y is

known for an input vector x. In order to associate

data from the input space X to the output space

Y, the system must be enriched with supervised

learning abilities.

2. The system must interact with negotiators, in

order to trace dissatisfaction caused by the

application of a routine, request additional

planning and decision making and allow the

insertion of new knowledge to the system.

External evaluation by the negotiators results in

breaking the rigidity of routines. This feature

demands the development of a communication

protocol between the system and the negotiators,

as well as an adaptive structure to allow for

efficient knowledge insertion.

3. The system must be able to adapt to new data of

unknown distribution. We must take into account

that it may develop in its own machine learning

space M, different from the original data space Z.

New data vectors may have more or fewer

dimensions, resulting to dimensionality change

of the problem space over time. Therefore we

consider an open problem space.

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253

4. System adaptation must be fast and not require

many iterations.

We propose a model that consolidates the above

characteristics and stems from the integration of an

intelligent agent and an Evolving Connectionist

System (ECOS). “An ECOS is an adaptive,

incremental learning and knowledge representation

system that evolves its structure and functionality,

where in the core of the system is a connectionist

architecture that consists of neurons and connections

between them” (Kasabov, 2007). We outline the

learning abilities of ECOS that position them as top

candidates for the combination of knowledge

exploitation and exploration.

1. “ECOS may evolve in open space, where the

dimensions of the space can change”

2. “They learn via incremental learning, possibly in

an online mode”

3. “They may learn continuously in a lifelong

learning mode”

4. “They learn both as individual systems and as

evolutionary populations of such systems”

5. “They use constructive learning and have

evolving structures”

6. “They learn and partition the problem space

locally, thus allowing for a fast adaptation and

tracing the evolving processes over time”

7. “They evolve different types of knowledge

representation from data, mostly a combination

of memory-based, statistical and symbolic

knowledge”.

A simple ECOS system is eMLP (Kasabov, 2007),

which is a three-layer network with two layers of

connections. The first consists of input variables,

which represent the negotiation context. The second

contains rule nodes that evolve and allow for layer

growth during training. The rule nodes represent the

mapping of input to output space. These mappings

may be considered as IF-THEN rules, IF

<negotiation context> THEN <routine>.

The similarity of input vectors, which is a

precondition to routine application, is calculated

through the normalized distance between the input

vector and the incoming weight vector of the rule

node. Activation for the rule node is given through

A = 1 – Distance, which indicates that the more

similar the input vector (current negotiation context)

is to a previous case, activation tends to 1. A

sensitivity threshold may be used for the definition

of similarity. Supervised learning is also applied,

and the normalized output error is calculated. The

third layer represents the values of the output

variables, constituting the routines. The model

structure facilitates the accommodation of new

training examples within the evolving layer either by

modifying connection weights or by adding new

nodes. eMLP is suitable for online output space

expansion, because it tunes only the connection

weights of the local node and does not require

retraining of the whole system as in traditional

neural networks. In order to add a new node to the

output layer, the structure of the eMLP need to be

modified to accommodate the output node. The

modification affects only the output layer and its

connections with the evolving layer. As a result of

the training process new nodes will be added to the

evolving layer to represent new input-output

associations. The architecture of eMLP is illustrated

in figure 1, indicating the growing structure of the

evolving and output layer.

Figure 1: The evolving structure of eMLP (reproduced

with permission).

In order to meet all of the desirable

characteristics described above, the system must be

enriched with the ability to communicate with the

negotiators and receive external utility measurement

or even be provided with new information, evolving

the established routines. Our suggestion concerns the

integration of the eMLP with an intelligent agent,

which will act as a mediator providing negotiation

routines to the negotiator. The mediator will collect

previous negotiation knowledge from the negotiator

and train the eMLP which will be the agent core.

The communication protocol will support requests

for routine acquisition generated by the negotiator,

and routine proposals by the mediator. After the

application of each routine, the negotiator will

proceed to evaluation (by using a utility function)

and in case of dissatisfaction, the routine will break

and the negotiator will be challenged to apply his

skills for knowledge exploration. The new

knowledge will be provided back to the mediator

and result to the evolution of the eMLP.

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5 EXPECTED RESULTS

Our research attempts to shed light to the field of

negotiation optimization, by focusing on the

acquisition and application of negotiation routines.

It is commonly stated that the main problem of

routinization is the risk of applying ineffective

routines. As discussed earlier, among the causes that

produce inflexible routines lays a repetitive

mechanism that prohibits further questioning or

evaluation of the acts. We trust that our framework

makes contribution to theory of negotiation routines

by presenting two essential directions for breaking

routine rigidity. The first concerns the identification

of the need of external evaluation after each routine

has been applied and the second concerns the

enrichment of the system core with an evolving

structure that adjusts its functionality to

accommodate new knowledge. The first direction

actually indicates the necessity to keep an open

communication channel with the environment, and

allow for the notification and triggering of routine

change. The second direction concerns the

identification of an appropriate structure that

embraces evolving processes, in order to absorb new

knowledge and proceed to routine change.

To this extent we have outlined four desirable

characteristics in section 4 that are met in our

proposed framework. The first concerns a hypothesis

of the representation of negotiation context and

routines as vector pairs. The second identifies the

need of external routine evaluation and dynamic

system structure. Our framework suggests the

integration of the dynamic system structure (ECOS)

with a negotiation agent, in order to allow the

interaction of the model that captures knowledge and

extracts routines with the negotiators, and be

notified in terms of a utility measure (after routine

application). The mediator agent will be motivated,

if the utility decreases, to request further planning by

the negotiators. The results (new knowledge) will

then be inserted to the dynamic structure. The third

and fourth characteristics concern the system’s

ability to develop in open space and quickly adapt to

the environment. These characteristics are met by

the ECOS structure, since it learns and partitions the

problem space locally. Furthermore in section 3 we

have identified several limitations posed by the

application of AI techniques in evolving processes.

These are addressed by ECOS algorithms, since they

apply fast one-pass learning (adaptation) and are

resistant to catastrophic forgetting (Kasabov, 2007).

Their structure is simple and grows in terms of

adaptation to the environment (the eMLP grows by

the addition of new nodes to the evolving and output

layer). Finally ECOS systems evolve different types

of knowledge representation from data; therefore our

representational hypothesis is not limited.

This framework is completely new and will be

tested in several stages of negotiation to provide

support to negotiators by the extraction of routines.

We trust that since the system is evolving it will lead

to efficient results in prenegotiation processes

(strategy formulation, commitment to rules,

opponent observation, issues and problem

formulation, other prenegotiation convensions), as

well as in negotiation processes (decision of

proposal and negotiation locutions). We have made

the hypothesis that knowledge can be provided to

the system in pairs of x, y vectors therefore the

model is generic and can be used for the substitution

of parts or even whole negotiations. Our future

research concerns extensive tracing of negotiation

repetitive acts, and the development and application

of our model in those that can be modeled as vector

pairs. If in several cases uncertainty is introduced to

vector dimensions and we need to address the issue

of missing values, the framework can be extended to

contain evolving fuzzy neural network (EFuNN)

instead of simple eMPL in its dynamic structure.

EFuNNs, function as eMLPs but have two extra

connection layers that represent fuzzy input and

output spaces. For these cases we will investigate the

integration of EFuNNs to our mediator framework.

Model validation, in terms of the generalization

ability of the ECOS core to produce good results on

new, unseen data samples, can be implemented by

splitting the original dataset to train and test sets and

calculating the actual error of the system. The most

commonly stated validation methods are simple train

and test split of data, k-fold cross validation and

leave-one-out cross validation. Furthermore, the

proposed structure itself suggests external evaluation

of the applied routines by the negotiators, which

contributes in measuring the overall system

performance.

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