agent-based computing: promise and perils€¦ · agent-based computing: promise and perils nick...

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Agent-Based Computing: Promise and Perils

Nick JenningsDept of Electronics and Computer Science

University of [email protected]

http://www.ecs.soton.ac.uk/~nrj/

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Agent-Based Computing: A New Synthesis for AI and CS

Increasing number of systems viewed in terms of agents:

• as a theoretical model of computation n more closely reflects current computing reality than Turing

Machines

• as a model for engineering distributed software systemsn better suited than object-orientation, design patterns and

software architectures

• as a model for conceptualising and building intelligent entities n framework for unifying piecemeal specialisations

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But fundamental questions remain:

• why are agents an appealing computational model?

• what are the drawbacks of an agent-oriented approach?

• what are the wider implications for AI and CS of agentbased computing?

• what is the essence of agent-based computing?

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Which Perspective?

u Answer from many points of view• from philosophical to pragmatic

uProceed from standpoint of using agent-based software to solve complex, real-world problems

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Software Development is Difficult♦ The most complex construction task humans undertake

“Computer science is the first engineering discipline ever in which the complexity of the objects created is limited by the skill of the creator and not limited by the strength of the raw materials. If steel beams were infinitely strong and couldn’t ever bend no matter what you did, then skyscrapers could be as complicated as computers.” Brian K. Reid

u True whatever models and techniques are applied:“the essential complexity of software” Fred Brooks

u Software engineering provides models &techniques that make it easier to handlethis essential complexity

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The Continual Striving for Better Models and Techniques

♦ New approaches:

• component-ware

• design patterns

• application frameworks

• software architectures

• ……….

♦ Right direction, but:

• interactions too rigidly defined

• impoverished mechanisms for representing organisational context

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The Adequacy Hypothesis

Agent-oriented approaches can significantly enhance our ability to model, design and build complex

(distributed) software systems

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Exemplar Complex Systems

♦ Air traffic control

♦ Network management

♦ Manufacturing control

♦ Business process management

Complexity from large number of parts, many interconnections &

problem dynamics

Not algorithmic / computational complexity

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Talk OutlineI. The Essence of Agent-Based Computing

II. Promise of Agent-Oriented Software Engineering

III. Perils of Agent-Oriented Software Engineering

IV. Conclusions

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“The intolerable wrestle with words and meanings.”

T. S. Eliot

“We seldom attribute good sense, except with those who agree with us.”

Duc de la Rouchefoucauld

The Essence of Agent-Based Computing

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“encapsulated computer system, situated in some environment, and capable of flexible autonomous action in that environment in order to meet its

design objectives” (Wooldridge)

Agent

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“encapsulated computer system, situated in some environment, and capable of flexible autonomousautonomous action in that environment in order to meet its


Agent

♦ control over internal state and over own behaviour

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“encapsulated computer system, situatedsituated in some environmentin some environment, and capable of flexible autonomous action in that environment in order to meet its


Agent


♦ experiences environment through sensors and acts through effectors

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“encapsulated computer system, situated in some environment, and capable of flexibleflexible autonomous action in that environment in order to meet its


Agent

♦ reactive: respond in timely fashion to environmental change♦ proactive: act in anticipation of future goals


♦ experiences environment through sensors and acts through effectors

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Definitional Malaise“My guess is that object-oriented programming will be what structured programming was in the 1970s. Everybody will be in favour of it. Every manufacturer will promote his product as supporting it. Every manager will pay lip service to it. Every programmer will practice it (differently). And no one will know just what it is.” (Rentsch, 82)

“My guess is that agent-based computing will be what object-oriented programming was in the 1980s. Everybody will be in favour of it. Every manufacturer will promote his product as supporting it. Every manager will pay lip service to it. Every programmer will practice it (differently). And no one will know just what it is.” (Jennings, 99)

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Multiple Agents

In most cases, single agent is insufficient• no such thing as a single agent system (!?)

• multiple agents are the norm, to represent:(Bond & Gasser)

n natural decentralisationn multiple loci of controln multiple perspectivesn competing interests

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Agent Interactions

♦ Interaction between agents is inevitable• to achieve individual objectives and to manage

inter-dependencies

♦ Conceptualised as taking place at knowledge-level • which goals, at what time, by whom, what for

♦ Flexible run-time initiation and responses• cf. design-time, hard-wired nature of extant approaches

paradigm shift from previous perceptions of computational interaction

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CustomerServiceAgents

Provide Quote

Agent-Based Business Process Management (ADEPT)

Serviceproducer

Serviceconsumer

ServiceN

ame

(Jennings et al., 96)

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CustomerServiceAgents

VetCustomer

Provide Quote


Customer Customer VettingVettingAgentsAgents

Serviceproducer

Serviceconsumer

A B C D n...ServiceN

ame


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CustomerServiceAgents Legal

Agents

VetCustomer

Provide Quote ProvideLegalAdvice

Cost&DesignNetwork



Serviceproducer

Serviceconsumer


ame

DesignAgents


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CustomerServiceAgents Legal

Agents

VetCustomer

Provide Quote ProvideLegalAdvice

SurveyPremisesCost&DesignNetwork



Serviceproducer

Serviceconsumer


ame

DesignAgents

SurveyAgents


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In ADEPT, Interaction = Negotiation

A1 A2

XX X

X

Negotiation Space

ProposalsCounter-Proposals

AcceptsRejects

(Faratin, Sierra and Jennings, 98)

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CostStart TimeEnd TimeQuality of ServicePenalty for Violation

Service Name

A1 A2

XX X

X

Negotiation Space


AcceptsRejects


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CostStart TimeEnd TimeQuality of ServicePenalty for Violation

Service Name

A1 A2

XX X

X

Negotiation Space


AcceptsRejects

Does not know A2’s:

- reservation values- resource constraints- outside options


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Why Negotiation?

♦ De facto means of coming to agreement between independent entities• cannot instruct an autonomous agent!

♦ Arrange inter-dependent activities (required services) according to prevailing situation:• short vs. long time to reach agreement• scarce vs. plentiful resource availability

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Agents act/interact to achieve objectives:• on behalf of individuals/companies• part of a wider problem solving initiative

underlying organisational relationship between the agents

Organisations

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Organisations in ADEPT

♦ Influence negotiation behaviour• Survey department accountable to design dept.• Peers in same organisation vs.

opponent in different organisation

♦Allow agents to be grouped• e.g. multiple designer agents working as a team

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Organisations (Generally)

Since this organisational context:• influences agents’ behaviour

n relationships need to be made explicit– peers – teams, coalitions– authority relationships

• is subject to ongoing changen provide computational apparatus for creating,

maintaining and disbanding structures

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A Canonical View

EnvironmentSphere of influence

AgentInteractions

Organisationalrelationships

(see also: Castelfranchi, Ferber, Gasser, Lesser, …..)

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IV. Conclusions

ü

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Making the Case: Quantitatively

0

10

20

30

40

50

Productivity Reliability Maintainability

Software Metrics

Objects Agents

“There are 3 kinds of lies: lies, damned lies and statistics”Disraeli

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Making the Case: Qualitatively

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Software techniques fortackling complexity


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(Grady Booch)

Tackling Complexity

♦Decomposition

♦Abstraction

♦Organisation

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Nature of complexsystems



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Complex Systems♦ Complexity takes form of “hierarchy”

•• notnot a control hierarchy• collection of related sub-systems at different levels of abstraction

♦ Can distinguish between interactions among sub-systems and interactions within sub-systems• latter more frequent & predictable: “nearly decomposable systems”

♦ Arbitrary choice about which components are primitive

♦ Systems that support evolutionary growth develop more quickly than those that do not : “stable intermediate forms”

(H. Simon)

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Canonical view ofagent-based computing



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Canonical view ofagent-based computing

Degree ofMatch



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The Match Process

1. Show agent-oriented decomposition is effective way of partitioning problem space of complex system

2. Show key abstractions of agent-oriented mindsetare natural means of modelling complex systems

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1. Decomposition: Agents

♦ In terms of entities that have:• own persistent thread of control (active: “say go”)

• control over their own destiny (autonomous: “say no”)

♦ Makes engineering of complex systems easier:• natural representation of multiple loci of control

n “real systems have no top” (Meyer)

• allows competing (sub-) objectives to be represented and reconciled in context sensitive fashion

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Decomposition: Interactions

♦ Agents make decisions about nature & scope of interactions at run time

♦ Makes engineering of complex systems easier:• unexpected interaction is expected

n all interactions need not be set at design time

• simplified management of control relationships between componentsn coordination occurs on as-needed basis between

continuously active entities

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2. Suitability of Abstractions

♦ Design is about having right models

♦ In software, minimise gap between units of analysis and constructs of solution paradigm• OO techniques natural way of modelling world

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Complex System Agent-Based System

Sub-systems

Sub-system components

Interactions between sub-systems and

sub-system components

Relationships between sub-systems

and sub-system components

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Sub-systems Agent organisations

Sub-system components





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Sub-system components Agents





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sub-system components:

“at any given level of abstraction, findmeaningful collections of entities that

collaborate to achieve some higher levelview” (Booch)

“cooperating to achieve common

objectives”

“coordinating their actions”

“negotiating to resolve conflicts”



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“cooperating to achieve commonobjectives”

“coordinating their actions”“negotiating to resolve conflicts”

Relationships between sub-systemsand sub-system components

- change over time- treat collections as single

coherent unit

Explicit mechanisms for representing &

managing organisational relationships

Structures for modelling collectives

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üAgent-oriented approaches can

significantly enhance our ability to model, design and build complex

(distributed) software systems

The Adequacy Hypothesis

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The Establishment Hypothesis

As well as being suitable for designing and building complex systems,

agents will succeed as a software engineering paradigm

[NB: will be complementary to existing software paradigms like OO, design patterns, …]

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Agents Consistent with Trends in Software Engineering

♦ Conceptual basis rooted in problem domain• world contains autonomous entities that interact to get things done

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♦ Conceptual basis rooted in problem domain

♦ Increasing localisation and encapsulation• apply to control, as well as state and behaviour

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♦ Conceptual basis rooted in problem domain

♦ Increasing localisation and encapsulation

♦ Greater support for re-use of designs and programs• whole sub-system components (cf. design patterns, component-ware)

n e.g. agent architectures, system structures

• flexible Interactions (cf. application frameworks)n e.g. contract net protocol, auction protocols

• legacy softwaren e.g. place “wrapper” around existing code to give it agent interface

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Agents Support System Development by Synthesis

An agent is a stable intermediate form• able to operate to achieve its objectives and

interact with others in flexible ways

construct “system” by bringing agents together and watching overall functionality emerge from their interplay

• well suited to developments in:n open systems (eg Internet)n e-commerce

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IV. Conclusions

üü

“The moment we want to believe something, we suddenly see all thearguments for it, and become blind to the arguments against it”

George Bernard Shaw

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Unpredictable Interactions

In general case:n patterns: who, what, and whyn outcomes: accept, reject, modify

decided at run-time by complex interplay of:

n agent’s internal staten environment’s (incl. acquaintances’) perceived staten organisational context

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Emergent Behaviour

♦Behaviour cannot be understood solely in terms of individual components

“the whole is greater than the sum of the parts”

♦Can arise in any complex system• not always a bad thing

n sometimes a better model of problem

• more scope because of flexible nature of agent problem solving and interactions

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Overcoming these Perils♦ Presently, can develop specific solutions for

specific problems• simple interaction protocols• rigid and pre-set organisational structures• limit nature and scope of agent interplay

♦ More generally, need better understanding of:

• impact of sociality and organisational responsibilities on individuals’ behaviour

• symbiotic relationship between individual and societal behaviour

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Finding the Right Level

♦ Learn from GOFAI• Newell elucidated Knowledge Level (KL) as an abstraction

for addressing fundamental issues in single agent systems

♦ Propose Social LevelSocial Level (SL) (Jennings, 94; Jennings & Campos, 97)

• new computer level situated above KL• provides social principles and foundations for agent-based

systems

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Dimension Description KL SL

System Entity to bedescribed

(asocial)Agent

Agent organisation

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SystemEntity to bedescribed

(asocial)Agent

Agent organisation

Components System’s primitiveelements

GoalsActions

Agents,Interaction channels,

Dependencies,Org. relationships

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(asocial)Agent

Agent organisation


GoalsActions



CompositionalLaw

How componentsare assembled Various

RolesOrganisation’s rules

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(asocial)Agent

Agent organisation


GoalsActions



CompositionalLaw

How componentsare assembled Various

RolesOrganisation’s rules

BehaviourLaw

How system’sbehaviour dependson composition &

components

Principle ofRationality

Principles of Organisational& Social Rationality

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(asocial)Agent

Agent organisation


GoalsActions



CompositionalLaw

How componentsare assembled

VariousRoles

Organisation’s rules

BehaviourLaw

How system’sbehaviour dependson composition &

components

Principle ofRationality

Principles of Organisational& Social Rationality

MediumElements processed

to obtain desiredbehaviour

Knowledge

Org. & Social obligationsMeans of influencing others

Means of changingorganisational structure

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Exploiting the Social Level

♦Science of Agent-Based Computing• provides comprehensive model for specifying and

understanding behaviour in multiple agent systems

♦Engineering of Agent-Based Systems• provides basis for agent-based tools and

methodologies

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IV. Conclusions

ü

üü

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Promise

Agent-based computing has potential to:

provide powerful metaphors, concepts & techniques for conceptualising, designing and implementing complex (distributed) systems

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Perils

♦ Insufficient understanding of:• impact of sociality on individual & collective behaviour• impact of organisational relationships on behaviour and

performance of individuals and societies

♦Remedied by:• distilling basis of computation in social settings

n Social Level characterisation is step towards this end

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Acknowledgements

♦ Members of QMW DAI Group♦ Mike Wooldridge♦ Cristiano Castelfranchi♦ Ed Durfee♦ Les Gasser♦ Carl Hewitt

♦ Mike Huhns♦ Vic Lesser♦ Joerg MÜller♦ Simon Parsons♦ Van Parunak♦ Carles Sierra

agent-based computing: promise and perils€¦ · agent-based computing: promise and perils nick...

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