TOWARDS PROBLEM SOLVING METHODS

IN MULTI-AGENT SYSTEMS

Paul Bogg

, Ghassan Beydoun

and Graham Low

University of New South Wales, Australia

University of Wollongong, Australia

Keywords: Knowledge engineering, Multi-agent system methodologies, Problem-solving methods.

Abstract: Problem Solving Methods (PSM) are abstract structures that describe specific reasoning processes

employed to solve a set of similar problems and have proved very effective at enhancing reuse and

extensibility in developing knowledge-based systems. We envisage that off-the-shelf PSMs can similarly

assist in the development of agent-oriented solutions using Multi-Agent Systems (MAS). A challenge

towards the effective use of PSMs in MAS is that current approaches to formulating PSMs do not

adequately address the complexity of problems to which agent-oriented systems are suited. Towards

addressing this, this paper focuses on providing an approach to guide developers in adequately formulating

PSMs for complex problem-solving where interactions are involved, such as in domains where negotiation

and cooperation are essential for solving a problem.

1 INTRODUCTION

The demand for agent-oriented software motivated

the creation of new development approaches, such

as INGENIAS (Pavon et al., 2005), Tropos

(Bresciani et al., 2004) and MOBMAS (Tran &

Low, 2008). None adequately addressed

extensibility, interoperability and reuse other than

(Beydoun et al., 2007; Tran & Low, 2008) where it

was argued that an ontology-based approach is

needed for a truly domain-independent agent-

oriented development.

Following the reuse paradigm promoted in

knowledge-based systems development (Schreiber et

al., 2001), the work in (Beydoun et al., 2006)

proposes a process that revolves around a domain-

dependant ontology to build individual agents with

problem-solving methods (PSMs). PSMs are high-

level structures describing a reasoning process

employed to solve general types of problems (Fensel

et al., 2002). Continuing the work in (Beydoun, Tran

et al., 2006), we envisage that engineering problem-

solving knowledge as domain-independent

ontology-based PSM structures is beneficial towards

achieving domain-independent agent-oriented

methodologies and systems. A library of these

PSMs would assist the development of agent-

oriented systems in domains where existing

problem-solving knowledge may be reused. A set of

modular, reusable problem-solving components has

the potential to reduce development costs and speed

up the development process. More specifically, this

paper investigates the role that task and problem-

solving knowledge play, arguing that current

approaches to PSMs do not adequately address the

complexity of problems to which agent-oriented

systems are suited. In particular, where problem-

solving software components are dependant on

interactions appropriate PSMs that address

interaction functionality have not been fully

investigated. This paper proposes an extension to

PSMs with an additional interaction dependency

construct through which interaction specific

problem-solving knowledge can be used.

Interaction-specific PSMs describe knowledge about

interactions for problem-solving, and how to design

methods to resolve complex problems where

interactions are necessary. Negotiation is used as

brief example of how PSMs may be used to design

MASs for interaction dependent problem-solving.

2 RELATED WORK

By using a domain ontology and an appropriate

PSM, it was envisaged that knowledge based

308

Bogg P., Beydoun G. and Low G. (2009).

TOWARDS PROBLEM SOLVING METHODS IN MULTI-AGENT SYSTEMS .

In Proceedings of the 4th International Conference on Software and Data Technologies, pages 308-313

DOI: 10.5220/0002252603080313

 SciTePress

systems can be easily developed as new problems

are encountered (Figure 1) (Studer et al., 1998).

Figure 1: As new problems arise, the PSM and the

ontology banks are used to construct suitable KBSs. An

ontology from the ontology bank strengthens a given PSM

from the PSM bank to suit the domain.

Recently, approaches have begun to address the

elicitation of PSMs from common problem-solving

knowledge. CommonKADS (Schreiber, Akkermans

et al., 2001), is a prominent approach which

provides a Task model, which provides a

hierarchical description of tasks, and an Expertise

model, which provides the method for achieving a

task. CommonKADS provides reusable task-specific

PSMs for composing the Expertise model to solve a

variety of pre-determined types of tasks (such as

diagnosis). Knowledge engineering also leverages

ontologies for eliciting and developing domain-

independent and reusable PSMs. One early

approach by (Fensel et al., 1997) tackled reusability

by incorporating ontologies for domain, task, and

PSM-specific knowledge. Another approach,

OntoKADS, extends CommonKADS by way of

introducing ontologies to comprise the expertise

model (Bruaux et al., 2005).

UPML (Fensel, Motta et al., 2002) encapsulated

previous approaches to describing general task and

problem-solving knowledge with general ontology-

based PSM structures. One limitation in UPML is

the absence in consideration given to PSMs for tasks

where multiple software components are required to

interact in order to solve a problem. For instance, e-

commerce problem-solving agents negotiate for

trade. Interaction-dependent problem-solving (such

as negotiation) is prevalent in agent-oriented

systems. To leverage the benefits of PSMs in AOSE,

PSM structures addressing interaction-dependent

problem-solving need to be developed. Recent

approaches to incorporating PSMs into agent-

oriented architectures have not addressed this.

MAS-CommonKADS (Iglesias & Garijo, 2005)

advocates task and problem-solving knowledge use

in its methodology. However, it presumes the

existence of PSM libraries suitable for complex,

interaction-dependent problem-solving. The ORCAS

framework (Gómez & Plaza, 2007) introduces

methods to adapt PSMs to agent capabilities. Their

work addresses cooperation as “agent teams” at the

knowledge level. However, it doesn’t address

interaction dependent problem-solving knowledge

required for negotiation. This is the focus here.

Figure 2: Ontology-based MAS development using PSMs:

(1,2) Domain Ontology produces Goal Analysis is used to

select PSMs from a PSM bank. (3, 4) Knowledge analysis

delineates local agent knowledge. (5, 6).

In (Beydoun, Tran et al., 2006), software

engineering requirements to use PSMs were mapped

out resulting in a methodological model (Figure 2).

This work did not address the issue of how to best

formulate the PSMs for interaction-dependent

problem solving. This paper continues this work by

formulating an appropriate way to construct PSMs

for distributed multi-agent systems (MAS). Much

previous work has gone into integrating ontologies

and PSMs e.g. (Fensel, Motta et al., 1997). It is not

yet clear whether that work needs to be extended for

the integration of domain ontologies with PSMs for

MAS. Invetigating this is left as future work.

3 FORMULATING PSMS FOR

MAS

Three types of knowledge are consistently identified

in formulating a PSM structure (e.g. in (Decker et

al., 1999; Fensel, Motta et al., 2002)): domain

knowledge, task knowledge, and problem-solving

knowledge. In these terms, PSMs are structured

problem-solving knowledge suited to achieving

tasks/goals in particular domains. UPML (Fensel,

Motta et al., 2002) defines a PSM in terms of these

knowledge components. Complex distributed

problems to which MASs are suited to solve may

TOWARDS PROBLEM SOLVING METHODS IN MULTI-AGENT SYSTEMS

309

require interactions between agents to coordinate

solutions. Towards MAS-specific PSMs, this section

extends the UPML PSM definitions. It adds a new

construct notation, interaction dependencies, noting

that when multiple agents are required to solve a

particular problem then further analysis is required

to determine what type of interaction is necessary.

When problem-solving depends on interactions,

further consideration needs to be given towards

understanding how different PSM definitions are

related. This needs to be accounted for in order to

properly formulate PSMs for MAS. For instance

(Fig. 3), in designing two agents required to

coordinate building a house, PSMs for a carpentry

agent may depend on PSMs for a brick layer agent.

Where this type of relationship between PSM

definitions exists, we use the term PSM co-

dependency. Where co-dependencies exist between

PSM definitions for separate agents, we use the term

PSM interaction dependency to specifically mean

that agents may be required to interact with one

another in order to successfully solve problems.

Figure 3: Agent-level PSM composition.

Interaction dependent PSMs bring additional

dynamics to a MAS software development process.

Firstly, interaction dependent PSMs suggest the

presence of additional methods and/or agents during

an analysis phase. Secondly, interaction dependant

PSMs may assist in designing the interaction

structure between agents by suggesting what type of

exchange is required between agents. The type of

exchange required might be as simple as an enquiry,

or as sophisticated as negotiation. Thirdly, since

interaction dependant PSMs are ontology-based,

reuse (as suggested in (Breuker, 1999)) is a natural

feature for future MAS development.

From an individual agent-level perspective, for

distributed problems in which agents are required to

interact, an interaction dependent PSM may be

aimed at achieving agent-level goals. For instance, a

negotiating agent may have a ‘Buy Item PSM’ that

depends on negotiation to satisfy an agent-level goal

to acquire a good. However, a software engineer

may not only be interested in agent-level goals, but

may also be interested in system-level goals.

From a system-level perspective, another type of

relationship may exist between PSMs. As is

illustrated in interaction-dependent problem-solving

literature (such as in negotiation (Jennings et al.,

2001)), sometimes the software engineer is

interested in designing agents whose interactions

produce system-level properties. For instance,

optimal utilitarian agreements can be engineered by

designing negotiating agents to use a correct

combination of strategies under particular

circumstances (e.g. (Fatima et al., 2004)). A PSM

approach may be used to engineer systems where the

selection of Strategy PSM ‘A’ suggests that the

selection of another Strategy PSM ‘B’ facilitates

system-level properties (such as utility optimisation)

in addition to agent-level goals. PSMs with system-

level dependencies may be used to design agent

interactions such that system-level goals are

achieved without resorting to “agent teams” (e.g.

(Gómez & Plaza, 2007)) – coordination and

cooperation are achieved at the agent-level. (Müller,

2002) argues that this may produce agent-oriented

systems more widely applicable to general types of

problems.

Table 1: Examples of interaction dependent PSMs.

PSM PSM Interaction Dependency

Buy Item Sell Item Negotiation for trade

Compensate

for failure of

agent Y

Compensate

for failure of

agent Y

Coordination to continue

system operation during

component failure

Procure

Service for

consumer

Provision

Service to

consumers

Negotiate terms of service

agreement

Examples of interaction dependent PSMs are

provided in Table 1. The first example is PSMs for

commercial activities requiring interactions to

achieve individual agent goals. The type of

interaction required may be a simple retail exchange,

or be a complex multi-issue negotiation. A system-

level goal might be that all agent-level interactions

are optimal according to some criteria (e.g.

utilitarian optimal in (Fatima, Wooldridge et al.,

2004)). The second example may appear in MASs

where robustness is an important system-level

requirement. PSMs may be needed to design

coordinative actions assuring compensation during

component failure (for instance, sensor agents

compensate for the loss of other sensor agents in a

battlefield information system (Deloach et al.,

2008)). The third example may occur where agents

procure service level agreements (for instance, in

acquiring satellite and cable channels for television

viewing, such as in (Cattoni et al., 1999)).

These knowledge engineering-based guidelines

may be used in designing PSM repositories for

interaction dependent problem-solving knowledge

for use in AOSE. During AOSE analysis PSM

House Frame

PSM

Acquire Wood

Frames PSM

Build Brick W all

PSM

Acquire Bricks

PSM

Brick Layer Agent

Interaction

Dependency

Co-dependency

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repositories may not contain all relevant PSMs, and

may need to be refined or developed – this process is

not described here, and left as future work.

4 INTERACTION-SPECIFIC

PSMS

Applying the insights of the previous section, this

section adds new constructs to UPML to

accommodate complex interactions used to

formulate our new type of PSMs, interaction-

specific PSM. This assists the designers of MASs by

providing a structure to interaction-specific problem-

solving knowledge. This new type is needed

wherever interaction dependent PSMs suggest the

exchange between two agents is sophisticated (such

as negotiation, coordination or cooperation).

Interaction-specific PSMs are intended to be

reusable. Knowledge about interaction-dependent

problem-solving is reusable in different domains,

and for different tasks e.g. similar methods for

negotiation in e-commerce trade might be adopted in

the negotiation of free trade agreements. We use

literature on designing agents for negotiation,

cooperation, and coordination to identify three types

of interaction-specific PSMs (Fig. 4):

• Interaction Protocol PSM: defines the rules for

interaction engagement. An interaction protocol

defines an order to engagements between agents

using terms expressed by the communication

protocol.

• Model PSMs: structured knowledge about how

to model information that an agent observes.

They directly relate to interactions because

agency requires autonomous assessment of

itself, external agents and the environment. An

interaction protocol may constrain the types of

information an agent may observe.

• Strategy PSMs: structured knowledge about

how interactive behaviour is derived from

output from the Model PSMs and the

Interaction Protocol PSM.

The above three types are derived from

classifications of agent design components used for

interaction-dependent problem-solving (such as

described in (Sandholm, 1999; Jennings, Faratin et

al., 2001; Lomuscio et al., 2001)). For example,

(Sandholm, 1999) describes variations of interaction

protocols where particular strategies depend on

models of utility for cooperative distributed

problem-solving. (Lomuscio, Wooldridge et al.,

2001) describes interaction protocols and strategies

as the two basic types of components for agent-

based negotiation. (Jennings, Faratin et al., 2001)

describes areas of negotiation research concerned

with protocols, negotiation objects, and decision

making models.

Figure 4: Knowledge level PSM composition.

Interaction Protocol PSM definitions are refined

by domain and task knowledge to produce specific

Interaction Protocol mappings (Figure 4). Model

PSM definitions are refined by domain knowledge to

produce Model PSM mappings, whereby inputs to

these mappings are provided by the agent. Multiple

Model PSMs may be selected or refined, depending

on agent design. The output from the Interaction

Protocol and Model PSM mappings are then used to

select the Strategy PSM. The strategy PSM is then

refined by task knowledge to produce the Strategy

PSM mapping. The output of the Strategy PSM

mapping is then used by the software engineer to

design the agent’s next interactive move. By

distinguishing between Interaction Protocol PSMs,

Model PSMs, and Strategy PSMs, interaction

specific problem-solving knowledge may be reused

by software engineers to design agent-oriented

solutions to complex problems.

5 APPLICATION OF PSMS FOR

MAS DEVELOPMENT

This section describes an application of interaction

dependent PSMs and interaction-specific PSMs to

designing agents for negotiation. The methodology

follows from Section 2, Figure 2 (from (Beydoun,

Tran et al., 2006)), where ontology-based

development of MASs from PSMs was described.

The scenario is negotiation for e-commerce trade.

Example:

An agent oriented system is required

to automate negotiation in an electronic market

place for buying and selling fish. Autonomous, self-

interested agents act on behalf of people. Agents

TOWARDS PROBLEM SOLVING METHODS IN MULTI-AGENT SYSTEMS

311

determine when and how to negotiate in order to

satisfy the needs of people. Agents are responsible

for collecting relevant information, and negotiating

the best possible utility-based outcome given the

information context (Cuní et al., 2004).

Suppose a software engineer aims to design an

agent that buys fish. At the conclusion of a domain

ontology and goal analysis, the software engineer

establishes a set of goals and task requirements to be

satisfied by an agent. The engineer selects the task

“Buy fish” and identifies “Buy Item PSM” as an

appropriate possible solution. “Buy Item PSM” is

identified as having an interaction dependency with

another PSM, “Sell Item PSM”. Figure 5 illustrates

a PSM approach to designing the agent solution.

Figure 5: Agent-oriented modelling for fish market place

derived from PSMs with an interaction dependency.

The software engineer determines that

negotiation is the interaction type necessary for the

domain. Since negotiation is pervasive in many

domains, the engineer consults libraries for existing

negotiation-specific problem-solving knowledge.

Task and goal analysis revealed that agents are also

required to maximise a utility, where a utility is

defined by the domain ontology. Appropriate

interaction-specific PSMs need to be selected – a

type of Interaction Protocol, Model, and Strategy

PSM. The software engineer attempts to find

interaction-specific PSMs (within the repositories)

oriented towards utility modelling and strategy.

“Utility Modeling PSM” and “Maximise Utility

Strategy PSM” are identified. For defining the

interaction, a “Bargaining Protocol PSM” is suitable.

To complete the development, the software

engineer now needs to design the fish buying agent

for the market place. PSMs are refined by task and

domain knowledge, resulting in mappings that are

task and domain specific methods that can directly

be used to design agent plans. Firstly, the domain

ontology is used to refine the “Utility Model PSMs”

to produce a fish market mapping, and a personal

fish-value mapping (the inputs for these mappings

might come from the person for whom the agent is

acting). Secondly, refinement of the “Maximise

Utility Strategy PSM” is made towards a specific

communication protocol ontology, producing a fish-

buying strategy mapping. The inputs for the fish-

buying strategy mapping are the outputs from the

fish market mapping and personal fish-value

mapping. Thirdly, refinement of the “Bargaining

Protocol PSM” is made towards the domain

ontology to produce a bargaining protocol mapping

which restricts interactions defined in terms of the

communication protocol ontology. At the conclusion

of this design, the software engineer may decide to

engage in a similar process for designing the fish

selling agent, with a view to (possibly) re-using

PSMs and mappings identified for the buying fish

agent. In addition to defining methods for

interaction-dependent problem-solving, interaction-

specific PSMs might also have dependencies with

other PSMs. For instance, suppose the Utility Model

PSM required information from external market

agents – further analysis of interactions (albeit

simple enquiries) may be necessary to design the

agent to acquire this information.

Interaction dependencies between PSMs and

interaction-specific PSMs drive the agent-oriented

development of fish auction agents by using

ontology-based domain, task, and problem-solving

knowledge engineering where re-use and

extensibility are supported.

6 CONCLUSIONS

The use of domain ontologies in AOSE has recently

been investigated e.g. (Iglesias & Garijo, 2005).

That work has been limited to the early phases of

system development. It is our contention that the

potentially knowledge intensive nature of the

analysis involved towards creating the software

components in a Multi Agent System suggests that a

knowledge centred approach throughout the whole

software development cycle is effective. This

approach is underpinned by reusable knowledge

models concomitant with an appropriate set of

reusable domain problem solving processes (aka

methods) that operationalise corresponding chunks

of knowledge as required by the requirements of the

system. (Beydoun, Tran et al., 2006) presented a

methodological model underpinned by the presence

of PSM repositories ‘appropriately’ represented.

This paper bridges the gap between that work and

Buy Item

PSM

Sell Item

PSM

Interaction Dependency

Utility

Model

PSM

Max. Utility

Strategy

PSM

Bargaining

Protocol

PSM

Fish

market

mapping

Personal

fish-value

mapping

Fish-

buying

strategy

mapping

Bargaining

protocol

mapping

refinement refinementrefinement

Fish market

domain

ontology

output

Offer

AGENT

DESIGN

AGENT

ANALYSIS

AGENT

INTERACTION

DESIGN

Protocol

rules

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the representation required to formulate the PSMs

for interaction-dependent problem solving. We

introduce new constructs to model the interaction

dependencies of PSMs, and these are used by

software engineers in the analysis of solutions to

complex problems where interaction is required.

We illustrated these constructs in a simplified

development of a negotiation-based system.

Further work is required to create a formal

underpinning of interaction-dependent PSMs We are

in the process of developing a PSM library

containing interaction-specific PSMs for supporting

the development of negotiation agents in a variety of

real-world domains. Future work will also identify

and integrate software process steps required within

an agent-oriented methodology.

ACKNOWLEDGMENTS

This work is supported by the Australian Research

Council.

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