1

Auctions

2

Auction Protocols

English auctions First price sealed-bid auctions Second best price sealed-bid auctions

(Vickery auctions) Dutch auctions

3

4

The Contract Net

R. G. Smith and R. Davis

5

DPS System Characteristicsand Consequences

DPS System Characteristicsand Consequences

Communication is slower than computation— loose coupling— efficient protocol— modular problems— problems with large grain size

6

More DPS System Characteristicsand Consequences

More DPS System Characteristicsand Consequences

Any unique node is a potential bottleneck— distribute data— distribute control— organized behavior is hard to guarantee (since no one node has complete picture)

7

The Contract NetThe Contract Net

An approach to distributed problem solving, focusing on task distribution

Task distribution viewed as a kind of contract negotiation

“Protocol” specifies content of communication, not just form

Two-way transfer of information is natural extension of transfer of control mechanisms

8

Four Phases to Solution,as Seen in Contract NetFour Phases to Solution,as Seen in Contract Net

1. Problem Decomposition

2. Sub-problem distribution

3. Sub-problem solution

4. Answer synthesis

The contract net protocol deals with phase 2.

9

Contract NetContract Net The collection of nodes is the “contract net” Each node on the network can, at different

times or for different tasks, be a manager or a contractor

When a node gets a composite task (or for any reason can’t solve its present task), it breaks it into subtasks (if possible) and announces them (acting as a manager), receives bids from potential contractors, then awards the job (example domain: network resource management, printers, …)

10

Node Issues Task AnnouncementNode Issues Task Announcement

Manager

Task Announcement

11

Idle Node Listening toTask AnnouncementsIdle Node Listening toTask Announcements

Manager

Manager

Manager

PotentialContractor

12

Node Submitting a BidNode Submitting a Bid

Manager

PotentialContractor

Bid

13

Manager listening to bidsManager listening to bids

Manager

PotentialContractor

PotentialContractor

Bids

14

Manager Making an AwardManager Making an Award

Manager

Contractor

Award

15

Contract EstablishedContract Established

Manager

Contractor

Contract

16

Domain-Specific EvaluationDomain-Specific Evaluation

Task announcement message prompts potential contractors to use domain specific task evaluation procedures; there is deliberation going on, not just selection — perhaps no tasks are suitable at present

Manager considers submitted bids using domain specific bid evaluation procedure

17

Types of MessagesTypes of Messages

Task announcement Bid Award Interim report (on progress) Final report (including result description) Termination message (if manager wants

to terminate contract)

18

Efficiency ModificationsEfficiency Modifications

Focused addressing — when general broadcast isn’t required

Directed contracts — when manager already knows which node is appropriate

Request-response mechanism — for simple transfer of information without overhead of contracting

Node-available message — reverses initiative of negotiation process

19

Message FormatMessage Format

Task Announcement Slots:— Eligibility specification— Task abstraction— Bid specification— Expiration time

20

Task Announcement Example(common internode language)

Task Announcement Example(common internode language)

To: *From: 25Type: Task AnnouncementContract: 43–6Eligibility Specification: Must-Have FFTBOXTask Abstraction:

Task Type Fourier TransformNumber-Points 1024Node Name 25Position LAT 64N LONG 10W

Bid Specification: Completion-TimeExpiration Time: 29 1645Z NOV 1980

21

The existence of a common internode language allows new nodes to be added to the system modularly, without the need for explicit linking to others in the network (e.g., as needed in standard procedure calling).

22

Applications of the contract Net

Sensing Task Allocation (Malone) Delivery companies (Sandholm) Market-oriented programming (Wellman)

23

Bidding Mechanisms for Data Allocation

A user sends its query directly to the server where the needed document is stored.

24

Environment Description

serveri

serverj

a clienta query

a document

area i area j

distance

25

Utility Function

Each server is concerned only whether a dataset is stored locally or remotely, but is indifferent with respect to different remote location of the dataset.

26

The Trading Mechanism

Bidding sessions are carried on during predefined time periods.

In each bidding session, the location of the new datasets is determined and the location of each old dataset can be changed.

Until a decision is reached, the new datasets are stored in a temporary buffer.

27

The Trading Mechanism - cont.

Each dataset has an initial owner (called contractor(ds)), according to the static allocation.For an old dataset - the server which stores it.For a new dataset - the server with the nearest

topics (defined according to the topics of the datasets stored by this server).

28

The Bidding Steps

Each server broadcasts an announcement for each new dataset it owns, and also for some of its old local datasets.

For each such announcement, each server sends the price it is willing to pay in order to store the dataset locally.

The winner of each dataset is determined by its contractor. It broadcasts a message, including: the winner, the price it has to pay, and the server which bids this price.

29

Cost of Reallocating Old Datasets

move_cost(ds,bidder):the cost for contractor(ds) for moving ds from its current location to bidder. (for new datasets, move_cost=0)

obtain_cost(ds,bidder):the cost for bidder for moving ds from its current location to bidder.

30

Protocol Details

winner(ds) denotes the winner of dataset ds. winner(ds)=

arg max bidder move(ds)=true price_suggested(bidder,ds) - move_cost(ds,bidder) none otherwise

31

Protocol Details - cont.

price(ds) denotes the price paid by the winner for dataset ds.

price(ds)= second_max bidder־SERVERS

{ price_suggested(s,ds) -move_cost(ds,bidder) }+ move_cost(ds,winner).

32

Bidding Strategies

Attribute:move(ds)=true ifsecond_max bidder־SERVERS

{price_suggested(bidder,ds) -

move_cost(ds,bidder)} Ucontractor(ds)

(ds,contractor(ds)).

33

Bidding Strategies - cont.

LemmaIf the winner server had bid its true value of storing the dataset locally, then it will have a nonnegative utility from obtaining it.

LemmaEach server will bid its utility from obtaining the dataset:price_suggested(bidder,ds)= Ubidder(ds,bidder) - obtain_cost(ds,bidder).

34

Bidding Strategies - cont.

TheoremIf announcing and bidding are free, then the allocation reached by the bidding protocol leads to better or equal utility for each server than does the static policy.

The utility function is evaluated according to the expected profits of the server from the allocation.

35

Usage Estimation

Each server knows only the usage of datasets stored locally.

For new datasets and remote datasets, the server has no information about past usage.

It estimates the future usage of new and remote datasets, using the past usage of local datasets, which contain similar topics.

36

Queries Structure

We assume that a query sent to a server contains a list of required documents.

This is the situation if the search mechanism to find the required documents is installed locally by the client.

In this situation, the server has to learn from the queries about its local documents, to the expected usage of other documents, in order to decide whether it needs them or not.

37

Usage Prediction

We assume that a dataset contains several keywords (k1..kn).

For each local dataset ds, and each server d, the server saves the past usage of ds by d, in the last period

Then, it has to predict the future usage of ds by d. It assumes the same behavior than in the past.

38

Usage Prediction - cont.

It is assumed that the users are interested in keywords, so the usage of a dataset is a function of the keywords it contains.

The simplest model is: when a dataset usage is the sum of the the usage of each of its keywords. However, the relationship between the keywords and the dataset may be different.

39

Usage Prediction - cont.

The server has to learn about usage of datasets not stored locally:

We suggest that it will build a Neural Network for learning the usage template of each area.

40

What is Neural Network

•A neural network is composed of a number of nodes, or units, connected by links.

•Each link has numeric weight associated with it.

•The weight are modified so as to try to bring the network’s input/output behavior more into line with that of the environment providing the input.

41

Neural Network - Cont.

. . .

Hidden layer

Input layer

Output layer

Output unit

Input unit

42

Structure of the Neural Network

For each area d, we build a neural network. Each dataset stored by the server in area d, is

one example for the neural network of d. The inputs of the examples contain, for each

possible keyword, whether it exist in this dataset, or not.

43

Structure of the Neural Network - cont.

The output unit of the Neural Network for area d, is its past usage of this dataset.

In order to find the expected usage of another dataset, ds2, by d, we provide the network with the keywords of ds2.

The output of the network is its predicted usage of ds2 by area d.

44

. . .

Structure of the NN

For a certain dataset, for each keyword k there is an input unit: 1 if the dataset contains k.0 otherwise.

Hidden layer

Output unit: the usage of the dataset by a certain area.

45

Experimental Evaluation - Results Measurement

vcosts(alloc) - the variable cost of an allocation, which consists of the transmission costs due to the flow of queries.

vcost_ratio: the ratio of the variable costs when using the bidding mechanism and the variable costs of the static allocation.

46

Experimental Evaluation Complete information concerning previous queries (still

uncertainty):

The bidding mechanism reaches results near to that of the optimal allocation (reached by a central decision maker).

The bidding mechanism yields a lower standard deviation of the servers utilities than the optimal allocation.

Incomplete information: The results of the bidding mechanism are better

than those of static allocation.

47

Influence of ParametersComplete Information, no movements of old datasets

As the standard deviation of the distances increases, vcost_ratio decreases.

auction results

0

0.5

1

1.5

unifo

rm10

0030

0050

00

distance standard deviation

vco

st

rati

o

vcost ratio

48

Influence of Parameters - cont.

When increasing the number of servers and the number datasets, vcost_ratio is not influenced.

query_price, answer_cost, storage_cost, dataset_size and retrieve_ cost do not influence vcost_ratio.

usage, std. usage, distance do not influence vcost_ratio.

49

vcost ratio

0.75

0.8

0.85

0.9

0.95

0.02

0.06 0.

10.

140.

18

epsilon

vcost ratio

`

As epsilon decreases, vcost ratio increases: the system behaves better.

Influence of Learning on the System

50

Conclusion

We have considered the data allocation problem in a distributed environment.

We have presented the utility function of the servers, which expresses their preferences over the data allocation.

We have proposed using a bidding protocol for solving the problem.

51

Conclusion - cont. We have considered complete as well as

incomplete information. For the complete information case, we have

proved that the results obtained by the bidding mechanism are better than those of the static allocation, and closed to the optimal results.

52

Conclusion - cont. For the incomplete information environment: We

have developed a neural-network based learning mechanism.

For each area d, we build a neural network, trained by the server of d.

By this network, we find expectation for other datasets, not currently stored by d.

We found, by simulation, that the results obtained are still significantly better than the static allocation.

53

Future Work

Future Work:Datasets can be stored in more than one server.Bounded rationality. Repeated game.

54

Reaching Agreements Through Argumentation

Collaborator: Katia Sycara, Madhura Nirkhe, Amir Evenchik, and

Ariel Stolman

55

Introduction

Argumentation--an iterative process emerging from exchanges among agents to persuade each other and bring about a change in intentions.

A logical model of the mental states of the agents: beliefs, desires, intentions, goals.

The logic is used to specify argument formulation and a basis for Automated Negotiation Agent.

56

Agents as Belief, Desire, Intention systems

Belief: information about the current world state subjective

Desire preferences over future world states can be inconsistent (in contrast to goals)

Intentions set of goals the agent is committed to achieve the agent’s “runtime stack”

Formal models: mostly modal logics with possible-worlds semantics

57

Logic Background

Modal logics; Kripke structures Syntactic Approaches Baysen Networks

58

Language: there is a set of n agents Kia --- i knows a Bia ----i believes a P -- a set of primitive propositions: P,QExamples: K1a

Semantics: A Kripke Structure consists of four elements:

A set of possible worlds P-----> {True,False}

n binary relations on the worlds ~1, ~2.…,

Modal LogicsModal Logics

59

Example of a Kripke Structure

W={w1,w2,w3} M,w1|= p & K1p

P,QQ

P

w1w2

w3

1

1

2 2

60

Axioms

Kia & Ki(a-->b) ---> Kib

If |-a then |-Kia

Kia -->a

Kia-> KiKia

~Kia --> Ki ~ Kia

~Bi false

Each axiom can be associated with a condition on the binary relation.

61

Problems in using Possible Worlds Semantics

Logical omniscience--the agent believes all the logical consequences of its belief.

The agent believes in all tautologies.

Philosophers: possible worlds do not exist.

62

Minimal Models: partial solution

The intension of a sentence: the set of possible worlds in which the sentence is satisfied

Note: if two sentences have the same intensions then they are semantically equivalent.

A sentence is a belief at a given world if its intension is belief-accessible.

According to this definition, the agent's beliefs are not closed under inferences; the agent may even believe in contradictions.

63

Minimal model: example

P QP ~Q

P ~Q

~P ~Q

64

Beliefs, Desires, Goals and Intentions

We use time lines rather than possible worlds. An agent's belief set includes beliefs concerning the world and

beliefs concerning mental states of other agents. An agent may be mistaken in both kinds of beliefs and beliefs

may be inconsistent. The beliefs are used to generate arguments in the negotiations. Desires: may be inconsistent. Goals: a consistent subset of the set of desires. Intentions serves to contribute to one or more of the agent's

desires.

65

Intentions

Two types: Intention-To and Intention-That Intention-to: refer to actions that are within the

direct control of the agent. Intention-that: refer to propositions that are not

directly within the agent's realm of control, that it must rely on other agents for satisfying-- can be achieved through argumentation.

66

Argumentation TypesArgumentation Types

A promise of a future reward. A threat. An appeal to past promise. Appeal to precedents as “counter example.” Appeal to “prevailing practice.” Appeal to self-interests

67

Example: 2 Robots

Two mobile robots on Mars each built to maximize its own utility.

R1 requests R2 to dig for a mineral. R2 refuses. R1 responds with a threat: ``If you do not dig for me, I will break your antenna''. R2 needs to evaluate this threat.

Another possibility: R1 promises a reward: ``If you dig for me today, I will help you move your equipment tomorrow.'' R2 needs to evaluate the promise of future reward.

68

Usage of the logic

Specification for agent design: the model constraints certain planning and negotiation processes. Axioms for argumentation types

The logic is used by the agents themselves: ANA (Automated Negotiation Agent)

69

ANA

Complies with the definition of an Agent Oriented Programming (AOP) system (Shoham): The agent is represented using notions of

mental states; The agent's actions depend on these mental

states;The agent's mental state may change over time; Mental state changes are driven by inference

rules.

70

The Block World EnvironmentThe Block World Environment

ח5 4 3 2 1

11

2

71

Mental State ModelMental State Model

Beliefs b(agent1,world_state([blockE / 5 / 1,blockD / 4 / 1,blockC / 3 / 1,blockB /

2 / 1,blockA / 1 / 1]),[0,2,t]).

Desires desire(desire1,0,agent1 ,[blockB / 6 / 2], 39,0). desire(desire2,0,agent1, [blockB / 1 / 1], 29,0). desire(desire3,0,agent1, [blockB / 6 / 1], 35,0). desire(desire4,0,agent1, [blockE / 2 / 1], 38,0).

Goals goal(agent1, 0, [[blockB / 6 / 2 / [desire1], blockE / 2 / 1 / [desire4]])

72

Mental State ModelMental State Model

Desired World desired_world([ [blockC / 3 / 1 /[unused_block],

blockA / 1 / 1 /[unused_block], blockD / 6 / 1 /[supporting], blockB / 6 / 2 /[desire1], blockE / 2 / 1 /[desire4]]).

Intentions intention(1,agent1,0,that,intention_is_done(agent1,0), [blockB /

2 / 1 / 7 / 1],0, [ towards_goals]). intention(2,agent1,0,to,intention_is_done(agent1,1), [blockD / 4

/ 1 / 6 / 1],0, [supporting]). intention(3,agent1,0, that,intention_is_done( agent1,2), [blockB

/ 7 / 1 / 6 / 2],0, [desire1]). intention(4,agent1,0,to,intention_is_done(agent1,3), [blockE / 5

/ 1 / 2 / 1],0, [desire4]).

73

Agent Infrastructure: Agent Life Agent Infrastructure: Agent Life CycleCycle

First PlanReading Messages

Planning next steps

Performing nextintentions

Dealing with the agent’s own threats

74

The Agent Life Cycle: Reading The Agent Life Cycle: Reading MessagesMessages

Types of messages Queue Waiting for answers Negotiation and world change aspects Inconsistency recovery

Figure 3.2 - Reading Messages Stage

NegotiationAspect

ReadMessage

WorldChangeAspect

75

The Agent Life Cycle:The Agent Life Cycle: Dealing with the agent’s own threats Dealing with the agent’s own threats

Detection Make abstract threats concrete Execute evaluation

Dealing with the agent’s own threats

Regular Threat Abstract Threat

76

The Agent Life Cycle: Planning The Agent Life Cycle: Planning next stepnext step

Mental states usage Backtracking Better than current state New state or dead end Achievable plan

GoalSelection

DesiredWorld

SelectionIntentionsGenerator

Planning next step

77

The Agent Life Cycle: Performing The Agent Life Cycle: Performing next intentionnext intention

Intention to - intention that Other agent listening? One argument per cycle.

Figure 3.5 - Perform next intention

Perform Intention-to

Generate and send an argument

78

Agent Definition Examples

Agent Type agent_type(robot_name, memory-less).

Agent Capability capable(robot, blockC / 3 / 1 / 4 / 1).

Agent Beliefsb(first_robot, capable(second_robot, AnyAction), [[0, t]]).

Agent Desiresdesire(first_desire, 0, robot, [blockA/3/1],

15, 1).

79

Agent Infrastructure: Agent Agent Infrastructure: Agent Parameters ListParameters List

Cooperativeness

Reliability (promises keeping)

Assertiveness

Performance threshold (Asynchronous action)

Usage of first argument

Argument direction

Knowledge about other desires

Knowledge about other capabilities

Measurement of other agent promises keeping

Execution of threats by another agent

80

A promise of a future rewardA promise of a future reward

Application conditions Opponent agent can perform the requested action. The reward action will help the opponent achieve a goal (requires knowledge

of opponent desires). Argument not used in the near past.

Implementation Generate opponent’s expected intentions. Offer one of the intentions as a reward:

– Mutual intention which opponent cannot perform by itself (requires knowledge of opponent capabilities).

– Opponent’s intention which it cannot perform.

– Any mutual intention.

– Any Opponent’s intention.

81

A threatA threat Application conditions

Opponent agent can perform the requested action. The threat action will interfere with the opponent’s achieving some goals

(requires knowledge of opponent desires). Argument not used in the near past.

Implementation Agent chooses best cube (requires knowledge of opponent capabilities). Agent chooses best desire. Agent chooses a threshing action:

– Moving out.

– Blocking.

– Interfering.

82

Request Evaluation Mechanism- Request Evaluation Mechanism- Parameters ListParameters List

DL (Doing Length)

NDL (Not Doing Length)

DTL (Doing That Length)

NDTL (Not Doing That Length)

PL (Punish Length)

PTL (Punish That Length)

DP (Doing Preference)

NDP (Not Doing Preference)

83

Request Evaluation Mechanism- Agent Request Evaluation Mechanism- Agent ParametersParameters

CP: The agent’s cooperativness.

AS: The agent’s assertiveness.

RL: The agent’s reliability.

ORL: The Other agent’s reliability for keeping promises.

OTE: The Other agent’s percentage of threat executing.

84

Request Evaluation Mechanism- The Request Evaluation Mechanism- The FormulasFormulas

A s i m p l e r e q u e s t

A c c e p t a n c e V a l u eN D L

D L

N D T L

D T L

D P

N D PC P

1

12

1

1

1

13

A n a p p e a l t o p a s t p r o m i s e

A c c e p t a n c e V a l u eN D L

D L

N D T L

D T L

D P

N D PC P R L

1

12

1

1

1

13

A p r o m i s e o f a f u t u r e r e w a r d

A c c e p t a n c e V a l u e

N D L

D L O R L R D

N D T L

D T L O R L R D

D P

N D PC P R L

1

1 1 12

1

1 1 1

1

13

W h e n a n a c t i o n i s c o n s i d e r e d t o b e a r e w a r d , R D ( R e w a r d ) i s e q u a l t o 1 , a n d 0 , i f n o t .

85

Request Evaluation Mechanism- The Request Evaluation Mechanism- The FormulasFormulas

A t h r e a t

A c c e p t a n c e v a l u e =

N D L P L N D L O T E

D L

N D T L P T L N D T L O T E

D T L

D P

N D P O T E N D P P PA S

1

12

1

1

1

13

A n a b s t r a c t t h r e a t

A c c e p t a n c e V a l u eN D L

D L

N D T L

D T L

D P

N D PA S

1

12

1

1

1

13

86

Experiments ResultsExperiments Results

Negotiating is better than not negotiating only where each agent has particular expertise.

Negotiating is better than not negotiating only where the agents have complete information.

Negotiating is better than not negotiating only for mutually cooperative agents or for an aggressive agent with a cooperative opponent.

Environment (game time, resources) effects the negotiations results.

87

Negotiations vs. no negotiationsNegotiations vs. no negotiations

When the agents that do not negotiate succeed in obtaining only 29.8% of their desires preference values, the negotiating agents succeed in obtaining 40.4%, on the average. (F=5.047, p<0.024, df=79).

Negotiations vs. No negotiations

0

10

20

30

40

50

Negotiation No negotiation

Su

cs

se

s

88

Complete information vs. no Complete information vs. no informationinformation

Agents that had no information succeed in obtaining a success rate of only 30.8%, while agents that had full information succeed in obtaining 40.4% on the average. (F=4.326,p<0.04,df=38).

Full information vs. no information

0

10

20

30

40

50

Full information No information

Su

cs

se

s

89

Using the first argument vs. using Using the first argument vs. using the best foundthe best found

Agents that used the first argument succeed in obtaining a success rate of only 34.8%, while agents that used the best argument succeed in obtaining 40.4% on the average, but this result is not significant. (F=2.28,p<0.138,df=38).

First vs. best argument

32

34

36

38

40

42

Uses best argument Uses f irst argument

Su

cs

se

s

90

Cooperative vs. Aggressive agentCooperative vs. Aggressive agent

23.5% : 41.4% (F=10.78,p<0.001,df=63).

Negotiations between cooperative and aggressive agents

0

1020

3040

50

Cooperative Aggressive

Su

css

es

91

Cooperative and Aggressive Agents vs. Cooperative and Aggressive Agents vs. No NegotiationsNo Negotiations

38.3% : 29.8% (F=6.01, p<0.019,df=35).

Cooperative agents vs. Non negotiating agents

0

10

20

30

40

50

Cooperative agents Non negotiating agents

Su

css

es

92

No negotiations VS. Aggressive Negotiation

Not negotiating vs. Agressive negotiations

0

10

20

30

40

No Negotiations Agressives

Su

css

es

38.3% : 20.8% (F=10.03, p<0.002,df=50).

93

Cooperative vs. aggressiveCooperative vs. aggressive

Aggressive vs. cooperative 41.4% Two cooperatives 38.3% No negotiation 29.8% Cooperative vs. aggressive 23.5% Two aggressive 20.8%

94

Environment ConstraintsEnvironment Constraints

Number of desires

0

10

20

30

3 Desires 6 Desires 9 Desires

Number of Desires

Su

css

es

95

Environment ConstraintsEnvironment Constraints

Time for game

16.7% : 21.7% (F=2.41, p<0.122, df=139).

05

10152025

60 Sec 120 Sec

Time for a Game

Su

cs

se

s

96

Is it worth it to use formal methods for Multi-Agent Systems in general and

Negotiations in particular?

97

Game-theory Based Frameworks(Non-cooperative Models)

Strategic-negotiation model based on: alternating offers model of rubinstein. Applications:Data allocation (schwartz & kraus AAAI97),Resource allocation , task distribution

(kraus wilkenfeld zlotkin AIJ95, kraus AMAI97),

hostage crisis (kraus wilkenfeld TSMC93).

98

Advantages and Difficulties:Negotiation on Data Allocation

Beneficial results; proved to be better than current methods; simple strategies.

Problems: Need to develop utility functions; Finding possible action: identifying optimal

allocations is NP complete; Incomplete information: game-theory

provides limited solutions.

99

Game-theory Based Frameworks(Non-cooperative Models)

Auctions applications:

Data allocation (schwartz & kraus ATAL97),Electronic commerce.

Subcontracting based on: principle agent models. Applications:

Task allocation (kraus, AIJ96).

100

Advantages and Difficulties:Auctions for Data Allocation

Beneficial results; proved to be better than current methods.

Problems: Utility functions,Applicable only when a server is concerned only

about the data stored locally, Difficult to find bidding when there is incomplete

information and the evaluations are dependant on each other: no procedures.

101

Coalition theories applications:

Group and teams formation (shehory &kraus CI99).

Benefits: well-defined concepts of stability; mechanisms to divide benefits.

Difficulties: utility functions, no procedures for coalition formation; exponential problems.

DPS model: combinatory theories & operations research (shehory &kraus AIJ98).

Game-theory Based Frameworks(Cooperative Models)

102

Multi-attributed decision making: application:Intentions reconciliation in SharedPlans

(grosz & kraus, 98). Benefits: using results of MADM, e.G.,

Specific method is not so important, standardization techniques.

Problems: choosing attributes; assigning values, choosing weights.

Decision-theory Based Frameworks

103

Logical Models

Modal logic: BDI models:applications:Automated argumentation's (kraus, sycara &

eventchick AIJ99).Specification of sharedplans (grosz & kraus AIJ96).

Bounded agents (nirkhe, kraus, perlis JLC97).Agents reasoning about other agents (kraus &

lehmann TCT88 kraus & subrahmanian IJIS95).

104

Advantages and Difficulties:Logical Models

Formal models with well studied properties:excellent for specification.

Problems: Some assumptions are not valid (e.g., omnicience).Complexity problems.There are no procedures for actions: required a lot of

programming; decision making; developing preferences.

105

Physics Based Models

Physical models of particle-dynamics Applications: Cooperation in large-scale multi-agent systems: freight deliveries within a metropolitan area. (Shehory & Kraus ECAI96 Shehory,

Kraus & Yadgar ATAL98). Benefits: efficient; inherits the physics

properties. Problems: adjustments; potential functions

106

Summary Benefits: formal models which have already

been studied; lead to efficient results. No need to invent the wheel.

Problems: Restrictions and assumptions made by game-

theory are not valid in real world MAS situations: extensions are needed.

It is difficult to develop utility functions.Complexity problems.