From Multiagent Systems to Multiagent Societies

Michael Berger

Based on:

1) “Multiagent Systems and Societies of Agents” / Michael N. Huhns and Larry M. Stephens

In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (Chapter 2) / Gerhard W. Weiss

2) “Commitments and Conventions: The Foundation of Coordination in Multi-Agent Systems” / Nick R. Jennings

Overview

• Agent and Environment

• Communications

• Interactions

• Commitments and Conventions

Part I:Agent and Environment

Agent - Definition

•An active object with the ability to

perceive, reason and act.

•Has explicitly represented knowledge and

a mechanism for operating on or drawing

inferences from its knowledge.

•Has the ability to communicate.

• Knowable (Accessible)

• Predictable (Deterministic)

• Controllable

• Historical (non-Episodic)

• Telelogical

• Real-time (Dynamic)

No

No

No

Yes

Yes

Yes

Open

Environment - Categories

Part II:Communications

Communications - Overview• Motivation

• Meanings

• Speech Acts

• Message Types and Dialogue Roles

• Communication Protocols

• KQML

• KIF

• Ontologies

Motivation (I)

• Coordination - the extent to which agents avoid extraneous activity.– Reducing resource contention

– avoiding livelock / deadlock

– maintaining safety conditions

• Coherence - how well the system behaves as a unit.– Determining shared goals

– Pooling knowledge and evidence

?

Motivation (II)

• Coordination - “not making things worse”.

• Coherence - “making things better”.

• Communication enables the agents to coordinate their actions and behavior, resulting in systems that are more coherent.

Meanings (I)

• Communication - consists of:

– Syntax - how the symbols of communication

are structured.

– Semantics - what the symbols denote.

– Pragmatics - how the symbols are interpreted.

• Meaning = Semantics + Pragmatics

Meanings (II)

• Dimensions of meaning:– Descriptive vs. Prescriptive

– Speaker’s vs. Hearer’s vs. Society’s Perspective

– Semantics vs. Pragmatics

– Contextuality

– Identity

– Cardinality

Speech Acts• Speech act theory used as basis for analyzing

human communication.• Theory views human natural language as

actions.• Speech acts have three aspects:

– Locution - the physical utterance by the speaker.– Illocution - the intended meaning of the utterance by

the speaker.– Perlocution - the action that results from the locution.

• “Performative” - Speech acts that have the property that “saying it makes it so” (e.g. promise, report, tell, request, demand).

Message Types and Dialogue Roles

• Two basic message types:– Assertion– Query

• Three dialogue roles:– Master (active)

• Sends queries (questions), receives assertions (answers), sends assertions (fact determinations).

– Slave (passive)• Receives queries (questions), sends assertions

(answers), receives assertions (fact determinations).

– Peer• Master + Slave

Communication Protocols

• Communication can be:– binary (single sender, single receiver)– n-ary (single sender, many receivers)

• Messages sent using communication protocols are specified by a data structure, that contains the following fields:– Sender– Receiver– Encoding / Decoding functions– Language of message– Message content

Communicating Agents (I)

a is broken.

KQML• KQML - Knowledge Query and Manipulation

Language.• Basic KQML performative defined by a structure

that contains the following fields:– Sender– Receiver– Language– Ontology– Content

• More advanced performatives.• Language used as wrapper for other languages -

Domain independent!• Forwarding and nesting possible.

Communicating Agents (II)Sender: Cowboy

Receiver: Shadow

Language: English

Content: a is broken

Languages: French Languages: English,

Spanish, Basque

Je ne comprends pas

KIF• KIF - Knowledge Interchange Format.

• Prefix version of first-order predicate

calculus.

– Example: or ((and (> ?a 6) (> b 5))) (< c 7)

• Possible to encode knowledge about

knowledge (second-order) and to describe

procedures.

Communicating Agents (III)Sender: Cowboy

Receiver: Shadow

Language: KIF

Ontology: Computers

Content: broken(a)

Languages: French, KIFLanguages: English,

Spanish, Basque, KIF

bad(message)

Ontologies: Computers,

Politics, Sports

Ontologies: Fashion,

Politics, Weather

Ontologies• Ontology - specification of objects,

concepts and relationships in an area of interest (domain).

• Concepts represented in first-order logic as unary predicates. Relationships represented by n-ary predicates.

• Note: predicates refer to classes of objects, not instances of objects.– except “instanceof”

• All agents share the same ontology - i.e. all agents use and understand the same “vocabulary”!

Communicating Agents (IV)Sender: Cowboy

Receiver: Shadow

Language: KIF

Ontology: Computers

Content: broken(a)

Languages: French, KIFLanguages: English,

Spanish, Basque, KIF

need_fixing(a)

Ontologies: Computers,

Politics, Sports

Ontologies: Fashion,

Politics, Weather, Computers

Computer Ontology:

instanceof(a, disk)

instanceof(X, disk) AND broken(X) ==> need_fixing(X)

Part III:Interactions

Interactions - Overview• Motivation

• Negotiation

• Market Mechanisms

• Contract Net

• Truth Maintenance Systems

• Blackboard Systems

Motivation• Communication is a necessary

condition for coordination and

coherence, but not a sufficient one.

• It would help if agents could:

– Determine shared goals

– Avoid unnecessary conflicts

– Pool knowledge and evidence

Negotiation• Negotiation - a process by which a joint decision

is reached by two or more agents, each trying to reach an individual goal.

• Main steps:– One of the agents communicates its initial position.– While no agreement is reached, each agent makes a

proposal in its turn. These may include:• Concessions.• New alternatives.

– Ends with agreement or disagreement.

• Mechanisms for negotiation may be:– Environment-centered– Agent-centered

Negotiation Mechanisms:Environment-Centered

• Environment designer.• “How can the rules of the environment be

designed so that the agents will interact productively and fairly?”

• A negotiation mechanism would ideally have the following attributes:– Efficiency– Stability– Simplicity– Distribution– Symmetry

Negotiation Mechanisms:Agent-Centered

• Agent designer.• “Given an environment, what is the best

strategy for my agent to follow?”• Large part of the negotiation mechanisms

assume that agents are economically rational.

• For example, a negotiation protocol that contains the following terms:– Deal– Utility– Negotiation set

Market Mechanisms (I)

• Everything of interest to the agents

described in terms of prices.

• Two types of agents:

– Consumers

– Producers

• Markets of goods are interconnected.

Market Mechanisms (II)

• Big market will usually reach a competitive

equilibrium:

– Consumers bid to maximize utility, subject to

their budget constraints.

– Producers bid to maximize profits, subject to their

technological capability.

– Net demand is zero for all goods.

Contract Net (I)• Interaction protocol for cooperative

problem solving.

• Modeled on the contracting mechanism

used by businesses.

• For any assignment, agents are divided

ad-hoc into managers and contractors.

• Managers:– Announce a task that needs to be performed.– Receive and evaluate bids from potential

contractors.– Award a contract to a suitable contractor.– Receive and synthesize results.

• Contractors:– Receive task announcements.– Evaluate their own capability to respond.– Respond (decline / bid).– Perform the task if bid is accepted by manager.– Report task’s results.

Contract Net (II)

1

5

69

2

3

47

8

Truth Maintenance System (I)

• Truth Maintenance System (TMS) - ensures the integrity of an agent’s knowledge, and keeps the knowledge base:– Stable

• Each datum that has a valid justification is believed.• Each datum that lacks a valid justification and which is not

in initial belief set is disbelieved.

– Well-founded• Permits no set of its beliefs to be mutually dependent.

– Logically consistent• No datum is both believed and disbelieved.• Every datum is either believed or disbelieved.• No data and its negation are both believed or disbelieved.

TMS Graph

Agent 2

Agent 1

P(IN)

Q(OUT)

T(INTERNAL)

R(IN)

S(OUT)

T(EXTERNAL)

U(OUT)

_

+

+

_

_

_

Truth Maintenance System (II)

• Every datum is labeled either:– IN (in initial belief set).– INTERNAL (“IN” because of local justification).– EXTERNAL (“IN” because another agent asserts it).– OUT (disbelieved).

• When justification is added or removed, the TMS is invoked:– Some data unlabeled, including the newly justified

datum and its consequences in all agents.– New Labeling introduced for all unlabeled data.– If any affected agent fails to label, backtrack occurs.

• Principal of TMS changes: Affect as few agents as possible and as few beliefs as possible.

TMS - Example (I)

Agent 2

Agent 1

P(IN)

Q(OUT)

T(INTERNAL)

R(IN)

S(OUT)

T(EXTERNAL)

U(OUT)

_

+

+

_

_

_

TMS - Example (II)

Agent 1

P(IN)

Q(OUT)

T(INTERNAL)

R(IN)

S(OUT)

T(EXTERNAL)

U(OUT)

_

+

+

_

_

_+

Agent 2

TMS - Example (III)

Agent 1

P

Q

T(INTERNAL)

R(IN)

S(OUT)

T(EXTERNAL)

U(OUT)

_

+

+

_

_

_+

Agent 2

TMS - Example (IV)

Agent 1

P

Q

T

R(IN)

S(OUT)

T

U

_

+

+

_

_

_+

Agent 2

TMS - Example (V)

Agent 1

P

Q

T

R S

T

U

_

+

+

_

_

_+

Agent 2

TMS - Example (VI)

Agent 1

P(OUT)

Q(OUT)

T(OUT)

R(OUT)

S(IN)

T(OUT)

U(IN)

_

+

+

_

_

_+

Agent 2

Blackboard Systems (I)• Akin to the following metaphor:

– A group of specialists working together on solving a

problem.

– A common blackboard allows every specialist to report

(“write down”) his sub-task results.

– Every specialist may be assisted in his work by

information reported on the blackboard.

• Every specialist is called a “knowledge source”

(KS).

Blackboard Systems (II)• Characteristics of blackboard systems:

– Independence of expertise.

– Diversity in problem-solving techniques.

– Flexible representation of blackboard information.

– Common interaction language.

– Event-based activation.

– Need for control.

– Incremental solution generation.

Part IV:Commitments and Conventions

Distributed Goal Search Model• Goals solution expressed as AND/OR graph

(which is directed and a-cyclic).– High-level goals are root nodes.– Primitive goals are leaf nodes.

• Graph also contains resources needed for solving primitive goals.

• Dependencies may exist between different goals or between a goal and its resource.– Strong vs. weak– Uni-directional vs. bi-directional

• Note that dependencies from resources to goals may be solved by adding more instances of the resource.

Distributed Goal Search Graph

Goals

G1 G2

G11 G1

2 G1k G1,2

m G2p G2

t

G11,1 G1

1,2 G1m,1 G2

m,2 G2p,1 G2

p,2

G1m,1,1 G1

m,1,2 G2p,1,1 G2

p,1,2 G2p,2,2

……………………….…

Agent1 Agent2

d11 d1

j d2j+1 d2

z…………………………………… ……………………………

Resources

Strong dependencies

Weak dependendcies

Interactions among Agents for Distributed Goal Search

• Defining the goal graph.

• Assigning particular regions of the graphs to

different agents.

• Controlling decisions about which areas of the

graph to explore.

• Traversing the graph.

• Ensuring that successful traversal of the graph

is reported.

Commitment - Definition

A pledge from one agent to another agent

(or itself) to undertake a specified

course of action.

Commitments• Practical reasoning agents employ intentions

for choosing a course of action - a kind of “self-commitment”.

• In computational problems, different agents commit themselves to solving different sub-goals of a larger goal.

• Agents may inform other agents of the sub-goals to which they are self-committed. In stronger terms, they may commit to other agents about solving these sub-goals.

Motivation for Conventions (I)

• Agents do not have complete knowledge of the goals and intentions of other agents.

• Infeasible to have all agents re-contemplate about the goals of other agents in every step:– Limited computation power– Limited communication bandwidth

• Infeasible to have one agent or database keep all information about all agents:– Bottleneck– Single point of failure

Motivation for Conventions (II)

• If circumstances changed, an agent might be

working sub-optimally until he asks about it.

– Another agent solves a goal

– Another agent commits itself to a goal

– Another agent drops his commitment to a goal

– Another agent discovers that a goal is no longer

attainable

• We still would like to keep a distributed system of

agents...

Convention - Definition

A pre-determined description, common to all

agents in the system, of the course of action

to be taken by an agent, given a specific

circumstance or occurrence.

Minimum Convention for Joint Commitments

• Formalism by Cohen and Levesque.

BASIC SOCIAL CONVENTION

REASONS FOR ACTION:

STATUS OF COMMITMENT TO SHARED GOAL CHANGES

STATUS OF COMMITMENT TO REACHING SHARED GOAL IN PRESENT TEAM CONTEXT CHANGES

STATUS OF COMMITMENT OF A TEAM MEMBER TO SHARED GOAL CHANGES

ACTIONS:

R1: IF STATUS OR COMMITMENT TO SHARED GOAL CHANGES OR

STATUS OF COMMITMENT IN PRESENT TEAM CONTEXT CHANGES

THEN INFORM ALL OTHER TEAM MEMBERS OF CHANGE

R2: IF STATUS OF COMMITMENT OF A TEAM MEMBER TO SHARED GOAL CHANGES

THEN DETERMINE WHETHER JOINT COMMITMENT STILL VIABLE

Convention for Limited-Bandwidth Environments

LIMITED-BANDWIDTH CONVENTION

REASONS FOR ACTION:

COMMITMENT SATISFIED

COMMITMENT UNATTAINABLE

MOTIVATION FOR COMMITMENT NO LONGER PRESENT

ACTIONS:

R1: IF COMMITMENT SATISFIED OR

COMMITMENT UNATTAINABLE OR

MOTIVATION FOR COMMITMENT NO LONGER PRESENT

THEN DROP COMMITMENT

R2: IF COMMITMENT SATISFIED

THEN INFORM ALL AGENTS WORKING ON RELATED GOALS

R3: IF COMMITMENT DROPPED BECAUSE UNATTAINABLE OR

MOTIVATION NOT PRESENT

THEN INFORM ALL AGENTS WORKING ON STRONGLY RELATED GOALS

R4: IF COMMITMENT DROPPED BECAUSE UNATTAINABLE OR

MOTIVATION NOT PRESENT AND COMMUNICATION RESOURCES NOT OVERBURDENED

THEN INFORM ALL AGENTS WORKING ON WEAKLY RELATED GOALS

Convention in Nearly Open Environments (I)

JOINT RESPONSIBILITY SOCIAL CONVENTION

INHERIT: BASIC SOCIAL CONVENTION

REASONS FOR ACTION:

SHARED GOAL IS MET

SHARED GOAL WILL NEVER BE MET

MOTIVATION FOR SHARED GOAL IS NO LONGER PRESENT

AGREED PLAN WILL NOT ACHIEVE DESIRED RESULTS

AGREED PLAN CANNOT BE EXECUTED

AGREED PLAN HAS NOT BEEN EXECUTED PROPERLY

ACTIONS:

R1: IF SHARED GOAL IS MET OR

SHARED GOAL WILL NEVER BE MET OR

MOTIVATION FOR SHARED GOAL IS NO LONGER PRESENT

THEN DROP COMMITMENT TO SHARED GOAL & TO AGREED PLAN

R2: IF AGREED PLAN WILL NOT ACHIEVE DESIRED RESULTS OR

AGREED PLAN CANNOT BE EXECUTED OR

AGREED PLAN HAS NOT BEEN EXECUTED PROPERLY

THEN DROP COMMITMENT TO AGREED PLAN

R3: IF DROP JOINT COMMITMENT TO AGREED PLAN AND

CAN RE-PLAN USING SAME AGENTS

THEN DEVELOP AND COMMIT TO NEW PLAN

Convention in Nearly Open Environments (II)

R4: IF DROPPED COMMITMENT TO AGREED PLAN AND

CANNOT RE-PLAN USING SAME AGENTS AND

CAN DEVELOP NEW PLAN USING DIFFERENT TEAM

THEN DROP COMMITMENT TO EXISTING TEAM & COMMIT TO NEW TEAM

R5: IF CANNOT DEVELOP NEW COMMON PLAN

THEN DROP COMMITMENT TO SHARED GOAL & TO AGREED PLAN

Possible Trends in Conventions

• The harsher the environment, the more rules

are needed to determine the agent’s action.

• The harsher the environment, the more

frequent are situations in which the agent

stops and reconsiders objectives.

– Similar to the spectrum between bold agents and

cautious agents (Kinny and Georgeff).

Example for Benefits of Conventions

Goals

G1 G2

G11 G1

2 G1k G1,2

m G2p G2

t

G11,1 G1

1,2 G1m,1 G2

m,2 G2p,1 G2

p,2

G1m,1,1 G1

m,1,2 G2p,1,1 G2

p,1,2 G2p,2,2

……………………….…

Agent1 Agent2

d11 d1

j d2j+1 d2

z…………………………………… ……………………………

Resources

Strong dependencies

Weak dependendcies

Agents without HonorG1,2

1

G11,1 G2

1,2

G1,21

G11,1 G2

1,2

G1k G2

m,1

G2m

Agent 1: G11,1 Agent 2: G2

1,2 Agent 1 reneges

Agent 1: G11,1 Agent 2: G2

p Agent 1 reneges

Agent 1: G1k Agent 2: G2

m Agent 1 reneges

G1k G2

m,1

G2m

Agent 1: G1k Agent 2: G2

m Agent 1 reneges

G1k,2 G2

m,1

G2m

Agent 1: G1k,2 Agent 2: G2

m Agent 2 reneges

G2m,2

G1k

G1k,1

Benefits of Conventions• Provides a degree of predictability to counteract

the uncertainty caused by the distribution of control.

• Mitigates the effect of commitments reneged.• Flexible - can sometimes be made at different

levels and thus have varied time horizons.– The lower the level, the higher the accuracy of

information and the larger are the required computation and communication bandwidth

– Lower levels don’t always provide a significant contribution

– Lower levels might cause more constraints

Conventions vs. Conventions

• Humans also have conventions.

– Not obligatory.

– Others don’t always expect adherence to them.

• Agent conventions are actually rules rather

than conventions.

The Dawn of Society• In a System of agents/humans acting without

conventions, they cannot expect anything from their peers.

• Pre-determined rules/conventions act as common denominator for all units.

• Conventions that are adhered to, allow the system to act more coherently without extra effort from particular units.– The whole is larger than the sum of its parts.

• Thus a system turns into a society.– Human societies always have unwritten rules.– Agent conventions also called “social rules”.

Thank You