Proceedings of STeP'96. Jarmo Alander, Timo Honkela and Matti Jakobsson (eds.),
Publications of the Finnish Artificial Intelligence Society, pp. 115-121.
An Object-Oriented Distributed
Problem Solving Environment
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This paper describes an object-oriented approach for building distributed problem
solving and decision support systems. The framework has been developed to solve
problems encountered in building decision support applications for business planning
tasks e.g. in the areas of strategic planning and budgeting, where the distribution of
decision making authority and the nature of the tasks favours the implementation of a
distributed support environment.
The framework is based on MUST Modeller(TM),
a flexible application development
and modelling tool developed by ViSolutions, Inc., in Helsinki, Finland.
The MUST development has been partially funded by
the Technological Development Centre of
the Finnish Ministry of Trade and Industry (TEKES).
Business decision-making problems encountered in larger organisations have
typically certain characteristics that motivate the employment of distributed problem
solving techniques, and can be used to derive requirements for the support system:
The decomposition of the overall task into a distributed problem-solving environment
reflects the organisational responsibilities. This division of the tasks and
responsibilities guides the design of the distributed problem solving agent
architecture. We have found examples of both task-related and organisational
hierarchy related agent compositions.
Our experience from practical applications is that the division of tasks and roles in
actual organisations is rather stable. Changes in organisation structure, in persons
holding different positions, or in objectives on different levels of the organisation due
to changes in market or competitive environment can necessitate immediate reactions
from the organisation and therefore from the decision support environment. If
essential changes in the business responsibilities between the corporate management
and division management would be made the design of the decision support system
might need to be revised. The basic application structure including inter-agent
interfaces for a given task can be assumed to be the same over several problem-
We have found the approach presented by Wooldridge and Jennings (Wooldridge-
Jennings 1994a) useful for positioning the efforts in the field of DAI (Distributed
Artificial Intelligence). To define and analyse problem solving agents in more detail,
the following dimensions can be seen:
- There are decision-making tasks that repeat themselves in basically the
same form within different periods.
- The members of the decision-making teams have roles that are related
to their tasks and positions within the organisation. These roles can
define the scope of allowed decisions and decision options, available
information and possible ability of readjusting assumptions of the
- The roles have typically hierarchical structures that reflect the
organisational positions and responsibilities of the decision makers.
The decision power associated with the roles is derived from these
positions. This determines the ability of setting and adjusting goals
and associated targets, and the set of decision options available for
each decision maker.
- The decision maker roles can often be used to guide the design of the
structure of the decision support environment. The different roles need
different support agents, and the inter-agent cooperation strategies
need to support in the achievement of the overall objectives set to the
decision-making team. Typically the agents support in the solution of
some sub-problem, whose assumptions and solution interact with those
of the other sub-problems.
- Each sub-solution is defined by a set of goals and constraints it must
satisfy. The detail level approach taken to local decisions is assumed
to be uninteresting from the global point of view, which means that the
decision makers are assumed to be autonomous within their
responsibility area as far as overall objectives are met.
- The decision making is asynchronous and can be distributed in time
and space. The progress of the problem-solving process requires
exchange of goal and sub-problem information between the agents.
The goal structure guides the local decision making to make decisions
that change the state so that it satisfies the goals.
- Each task at hand typically has time constraints and schedules, and a
solution (hopefully the best available by the time limit) is reached in
each case. For instance, a budget for an organisation is made by the
date it is handled and accepted by the board, and every effort is taken
to make a budget that satisfies the objectives set by the board.
Within this framework, we propose an agent architecture for business decision
support applications, based on an agent language, which in our case is based on the
MUST object-oriented modelling platform.
- Agent theories are mathematical and logical formalisms developed for
representing and reasoning about the properties of agents.
- Agent architectures are construction models and paradigms for the
implementation of computer and software agents and inter-operating
groups of agents.
- Agent languages are software systems for the construction of agents.
2. The Problem Solving Framework
The problem solving application framework consists of a set of agents implemented
as object models, communicating together to support the human decision makers in
their solution of decision support tasks in a coordinated fashion. In object-oriented
decision support applications the object model is used to define the domain model
containing application area specific knowledge and problem solving ability. To make
several such application agents cooperate in a useful way the agents must have a
problem-solving support model that maintains knowledge and awareness about the
problem solving tasks, their objectives, and the current situation in the model in
relation to these objectives. The framework consists of several agents, each
implemented as an object-oriented application, having the following structure:
The problem solving model contains the following elements:
Our approach is based on MUST Modeller, an object-oriented modelling and
application development tool (Lehto 1995). MUST has features that combine
benefits of declarative programming to the object oriented modelling environment:
- Definitions of the objectives represent the goals of the agent, and they
are used for evaluating each situation in relation to these goals. As the
object models of the problem-solving application are typically used by
a human decision maker the changes in the model are initiated by
actions of the user. The changes either move the subsolution modelled
by the application closer to its goal or further from it. Therefore it is
necessary to calculate in the application the distance from the goal in
possibly several dimensions so that the progress of the problem solving
can be enforced by useful feedback given to the user.
Another similar component is formed by the constraints set to the
agent by its problem-solving role, or the role of the agent or its human
user within the overall organisation. If some variable values within the
application can only be changed within preset limits or if these limits
are dynamically communicated by other agents, each agent needs a
mechanism to calculate the current situation in relation to the
constraints and give feedback to all attempted changes that would
violate the constraints.
- Definitions of the elements needed to monitor changes in the
application model that necessitate communication between the agents
are needed for initiating inter-agent communication when some
subresults are achieved, or when in some object model a change needs
further information, updating or querying the constraints, or passing of
information to other agents. These definitions are based on the
calculation dependency control capability of the calculation rule
language, and the ability of implementing method-like constructs using
- Definitions of the inter-agent communication control and behaviour
use information management system classes of MUST to produce the
contents of the messages sent to other agents and to interpret the
contents of the received messages.
Inter-agent communication is based on sending messages consisting of
object definition macros between the agent applications. In MUST,
each object (both the classes and instances) can produce a definition
macro that reproduces a similar object. When such a definition is
interpreted in the context of another agent application it creates a new
object that is defined by the class structure of the other application.
The object-oriented approach supports problem abstraction and representation of it in
a class structure. The structural and calculational properties of the problem domain
are declaratively defined as an integral part of the object structure. An essential
element of MUST's application building tools is the calculation rule language and its
dependency management and consistency maintenance methods. They provide the
tools for implementing the problem-solving agents' mechanisms for recognising the
changes in their knowledge base and their problem-solving status.
- The MUST objects have two kinds of attributes: typed data attributes
and relations, whose values are links to other objects. The relation
attributes provide an effective way for representing structural
information and dependencies between individual objects.
- MUST has a calculation rule language which is used to declaratively
define calculational properties of the attributes. The dependency
maintenance mechanism guarantees consistency of the model still
allowing delayed calculation for performance purposes.
- In addition to this declarative and functional nature the calculation rule
language has extensions for controlling the calculation and a set of
operations with side-effect, which can be used to create procedural
definitions. These features can be used to implement methods and
message passing structures. Together with the ability of monitoring
calculation and value invalidation this makes it possible to define
objects that monitor defined values and have a degree of awareness of
the calculational events in the model.
We have employed the framework in the implementation of decision support
applications in the areas of strategic planning and financial planning or budgeting.
Applications with their objectives and lessons learned have been described by
Paajanen (Paajanen 1996).
The evaluation of distributed problem solving applications can be made to consist of
the development of validity axioms and their proofs. As the framework is based on
an object-oriented tool utilising descriptive calculation rules, the axioms can be based
on those developed for the underlying modelling platform (Lehto 1995). Because the
object model is a descriptive representation of the logic related to the problem domain
and problem solving methods, the development of axioms that are required to hold
during the progress of problem solving is the basic approach to validation. Technical
elements of such validation schemes would include:
To validate a distributed problem solving system based on the proposed framework
one has to find a validation to each agent model and then be able to validate the
communication and communication based coordination schemes for the group of
agents. On the agent model level the declarative nature of the object model
definitions makes the effort of validation theoretically simpler than in the case of
procedural definitions. In addition, as the definitions are declarative, the increase of
complexity of the models does not necessarily complicate the validations, as each
declarative definition can be seen as an axiom stated about the model. To validate the
inter-operation of the agent models one should be able to find proofs for the correct
exchange of object definitions between the object models.
The agent level validation for the applications built using the framework presented
here can be seen to consist of two elements: the validation of the domain model and
the validation of the problem solving model. The validation of the domain model is
based on the specifications and functionality descriptions made with the application
users to define the required application characteristics. The essential requirement for
the problem solving model is that it correctly evaluates the state of the model against
the goals and handles the communication between different agents according to
interface definitions. A basic guideline for the validation is based on defining and
monitoring the following axioms about the problem solving model:
- Development of axioms the application model is required to satisfy
during all phases of problem solving.
- Development of traditional software validity proofs for procedural
parts included in side-effect calculation rules used to implement
methods attached to object classes.
- Analysis of the structure of the application model (both the domain
model and the problem solving model) to ensure that the model covers
all elements needed for the application.
- Analysis of the problem solving coordination and control strategies to
be able to ensure convergence towards a solution.
- Analysis of the functionality of the application especially in
comparison to predefined user expectations.
Basically the proof of the validity of these axioms in the framework of the previous
chapter is based on the validity of the underlying object model representations.
- At all times each agent knows the existing goals, their target values
and how the current comparison value is calculated, and can thus
calculate the difference between target and current values.
- Each agent has an ability to evaluate the progress of the problem
solving, and is able to measure its possible actions against that
- At all times each agent knows which goals are satisfied and which are
- At all times each agent knows which object definitions to send to
which other agents, and knows when transmission should be done. In
addition, each agent must know the communication channel to the
- At all times each agent knows which agents can send object definitions
to it and what are the communication channels it must check. As
communication can be asynchronous, the channels must provide a
buffering capability and the agents must be able to retrieve the
messages in chronological order.
- Decker, Keith S. - Lesser, Victor R. 1995:
Coordination Assistance for Mixed
Human and Computational Agent Systems. University of
Massachusetts Computer Science Technical Report 95-31, April 1995.
Submitted to the Second International Conference on Concurrent
Engineering Research and Application.
- Jennings, N. R. 1996:
Coordination Techniques for Distributed Artificial
Intelligence. In Foundations of Distributed Artificial Intelligence, pp.
187-210, (eds. G.M.P.O'Hare and N.R.Jennings), Wiley.
- Lehto, Teemu 1995:
Funktionaalisen oliopohjaisen sovelluskehittimen
suunnittelu ja toteutus (in Finnish). Master's Thesis 28.11.1995,
Helsinki University of Technology, Espoo, Finland.
- Paajanen, Martti - Tuomi, Ilkka 1992:
Adding Value to the Strategy Process
Using Object-Oriented Modelling and Software Support: Theoretical
Backgroung and Practical Results. 25th Annual Hawaii International
Conference on System Sciences, January 7-10, 1992.
- Paajanen, Martti 1996:
Distributed Problem Solving: Methodologies and a
Proposed Framework. Licentiate's Thesis 23.4.1996, Helsinki
University of Technology, Espoo, Finland.
- Rumbaugh, J. - Blaha, M. - Premerlani, W. - Eddy, F. - Lorensen, W. 1991:
Object-Oriented Modeling and Design. Prentice-Hall International.
- Tokoro, M. - Ishikawa, Y. 1984:
An Object-Oriented Approach to Knowledge
Systems. Proceedings of the International Conference on Fifth
Generation Computer Systems, 1984, pp. 623-631.
- Verkama, Markku - Hämäläinen, Raimo P. - Ehtamo, Harri 1992:
Multi-Agent Interaction Processes: From Oligopoly Theory to Decentralized
Artificial Intelligence. Group Decision and Negotiation, Vol. 1, No. 2,
1992, pp. 137-159, Kluwer Academic Publishers.
- Wooldridge, Michael J. - Jennings, Nicholas R. 1994:
Architectures, and Languages: A Survey. Proc. ECAI - Workshop on
Agent Theories, Architectures and Languages, pp. 1-32. (eds.
M.J.Wooldridge and N.R.Jennings), Amsterdam, the Netherlands
- Wooldridge, Michael J. - Jennings, Nicholas R. 1994b:
Cooperative Problem Solving Process. 13th International Workshop
on Distributed Artificial Intelligence (IWDAI-94), Lake Quinhalt, WA,
July 1994, pp. 403-417.
- Wooldridge, Michael - Jennings, Nicholas R. 1995:
Intelligent Agents: Theory
and Practice. The Knowledge Engineering Review, Vol. 10 (2), 1995,