From Multiagent Systems to Multiagent Societies

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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
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Overview

• Agent and Environment
• Communications
• Interactions
• Commitments and Conventions

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Part IAgent and Environment
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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.

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Environment - Categories

• Knowable (Accessible)
• Predictable (Deterministic)
• Controllable
• Historical (non-Episodic)
• Telelogical
• Real-time (Dynamic)

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Part IICommunications
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Communications - Overview

• Motivation
• Meanings
• Speech Acts
• Message Types and Dialogue Roles
• Communication Protocols
• KQML
• KIF
• Ontologies

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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

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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.

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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

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Meanings (II)

• Dimensions of meaning
• Descriptive vs. Prescriptive
• Speakers vs. Hearers vs. Societys Perspective
• Semantics vs. Pragmatics
• Contextuality
• Identity
• Cardinality

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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).

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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

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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

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Communicating Agents (I)
a is broken.
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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.

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Communicating Agents (II)
Je ne comprends pas
Sender Cowboy Receiver Shadow Language
English Content a is broken
Languages English, Spanish, Basque
Languages French
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KIF

• KIF - Knowledge Interchange Format.
• Prefix version of first-order predicate calculus.
• Example or ((and (gt ?a 6) (gt b 5))) (lt c 7)
• Possible to encode knowledge about knowledge
  (second-order) and to describe procedures.

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Communicating Agents (III)
Sender Cowboy Receiver Shadow Language
KIF Ontology Computers Content broken(a)
bad(message)
Languages English, Spanish, Basque, KIF
Languages French, KIF
Ontologies Computers, Politics, Sports
Ontologies Fashion, Politics, Weather
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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!

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Communicating Agents (IV)
Sender Cowboy Receiver Shadow Language
KIF Ontology Computers Content broken(a)
need_fixing(a)
Computer Ontology instanceof(a,
disk) instanceof(X, disk) AND
broken(X) gt need_fixing(X)
Languages English, Spanish, Basque, KIF
Languages French, KIF
Ontologies Computers, Politics, Sports
Ontologies Fashion, Politics, Weather, Computers
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Part IIIInteractions
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Interactions - Overview

• Motivation
• Negotiation
• Market Mechanisms
• Contract Net
• Truth Maintenance Systems
• Blackboard Systems

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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

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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

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Negotiation MechanismsEnvironment-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

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Negotiation MechanismsAgent-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

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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.

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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.

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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.

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Contract Net (II)

• 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 tasks results.

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9
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3
4
7
8
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Truth Maintenance System (I)

• Truth Maintenance System (TMS) - ensures the
  integrity of an agents 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.

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TMS Graph
U(OUT)
T(EXTERNAL)
_
Agent 2

P(IN)
T(INTERNAL)
_
_
Q(OUT)
R(IN)
S(OUT)

_
Agent 1
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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.

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TMS - Example (I)
U(OUT)
T(EXTERNAL)
_
Agent 2

P(IN)
T(INTERNAL)
_
_
Q(OUT)
R(IN)
S(OUT)

_
Agent 1
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TMS - Example (II)
U(OUT)
T(EXTERNAL)
_
Agent 2

P(IN)
T(INTERNAL)

_
_
Q(OUT)
R(IN)
S(OUT)

_
Agent 1
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TMS - Example (III)
U(OUT)
T(EXTERNAL)
_
Agent 2

P
T(INTERNAL)

_
_
Q
R(IN)
S(OUT)

_
Agent 1
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TMS - Example (IV)
U
T
_
Agent 2

P
T

_
_
Q
R(IN)
S(OUT)

_
Agent 1
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TMS - Example (V)
U
T
_
Agent 2

P
T

_
_
Q
R
S

_
Agent 1
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TMS - Example (VI)
U(IN)
T(OUT)
_
Agent 2

P(OUT)
T(OUT)

_
_
Q(OUT)
R(OUT)
S(IN)

_
Agent 1
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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).

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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.

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Part IVCommitments and Conventions
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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.

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Distributed Goal Search Graph
Agent1
Agent2
G1
G2
G11
G12
G1k
G1,2m
G2p
G2t

.
G11,1
G11,2
G1m,1
G2m,2
G2p,1
G2p,2
G1m,1,1
G1m,1,2
G2p,1,1
G2p,1,2
G2p,2,2

• Goals

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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.

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Commitment - Definition

• A pledge from one agent to another agent (or
  itself) to undertake a specified course of action.

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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.

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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

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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...

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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.

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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

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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

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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

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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

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Possible Trends in Conventions

• The harsher the environment, the more rules are
  needed to determine the agents 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).

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Example for Benefits of Conventions
Agent1
Agent2
G1
G2
G11
G12
G1k
G1,2m
G2p
G2t

.
G11,1
G11,2
G1m,1
G2m,2
G2p,1
G2p,2
G1m,1,1
G1m,1,2
G2p,1,1
G2p,1,2
G2p,2,2

• Goals

Strong dependencies
Weak dependendcies
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Agents without Honor
G1,21
Agent 1 G11,1 Agent 2 G21,2 Agent 1 reneges
G11,1
G21,2
G1,21
Agent 1 G11,1 Agent 2 G2p Agent 1 reneges
G11,1
G21,2
G2m
Agent 1 G1k Agent 2 G2m Agent 1 reneges
G1k
G2m,1
G2m
Agent 1 G1k Agent 2 G2m Agent 1 reneges
G1k
G2m,1
G2m,2
G2m
G1k
Agent 1 G1k,2 Agent 2 G2m Agent 2 reneges
G1k,2
G2m,1
G1k,1
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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 dont always provide a significant
  contribution
• Lower levels might cause more constraints

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Conventions vs. Conventions

• Humans also have conventions.
• Not obligatory.
• Others dont always expect adherence to them.
• Agent conventions are actually rules rather than
  conventions.

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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.

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Thank You