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CADIP 2002 Program. Jerry Stach, Ph.D. Eun Kyo Park, Ph.D. Agent Life Forms Laboratory School of Interdisciplinary Computing and Engineering University of Missouri – Kansas City. CADIP Project B iologic A gency for S earch of I ntractable S paces. Opening Soon at UMKC-ALFworld !.

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Cadip 2002 program

CADIP 2002 Program

Jerry Stach, Ph.D.

Eun Kyo Park, Ph.D.

Agent Life Forms Laboratory

School of Interdisciplinary Computing and Engineering

University of Missouri – Kansas City

CADIP Project

Biologic Agency for Search of Intractable Spaces

Opening Soon at UMKC-ALFworld!

Research vision
Research Vision

To develop a mobile, rational agent, capable of accepting a payload and by process or data driven signature, migrating to locations in a network to optimally complete its computational task.

Applicable domains
Applicable Domains

  • Software robots

  • Web Computing

  • Grid Computing

  • Document Processing

  • Intelligent Search

  • Collaborative Computing


  • CPU becomes band limited as problem scales

    • mobility decision/agent planning and reasoning time for site/service selection

    • Service Place executions implying control of arrival rates

    • communication between mobile agents and to situated agents (e.g. Traders)


  • Links become band limited as problem scales

    • message exchanges between agents, both situated and mobile

    • agent transportation (payload and code)

    • Trader Updates


provide computational autonomy, strong mobility and self regulation to damp bandwidth

  • adopt a biological (A-Life) computing model for the MAS

  • provide an artificial world infrastructure for agent computing

  • seek performance and scaling via emergent behaviors as opposed to policy and protocol

  • endow agents with social conscience (e.g. congestion avoidance when possible), preferences and rationality in decision making

Research problems
Research Problems

  • since there is no MAUF, perception is by characteristic function

  • fuzzy reasoning over Service Places

  • desirable population behaviors are emergent, not first order effects of policy

  • operating system and network support of strong mobility does not exist

  • existing agent architecture and design patterns are not robust enough to support architecture

Completed work
Completed Work

Mobility Decision Simulations

  • modeled optimal mobility decisions based upon graph theoretic solution

  • results provided experiential basis for proceeding to perception

  • simulator provides an observational basis for prediction of agent colony behavior and performance

  • results do not scale

Completed work1
Completed Work

WhitePaper on Strong Mobility

In strong mobility, not only code and data state are moved, but also the execution state, in order to restart the execution exactly from the point where it was stopped before movement.

Strong Mobility is frequently used in load leveling applications

Strong mobility
Strong Mobility

Strong mobility has the ability to store and retrieve computations as variables (continuations) and passes these to the other agents (remote continuations).

Strong mobility also usually communicates in an asynchronous fashion in which one agent sends messages to other agents but does not wait for answers.

Whenever one of the communication partners of an agent dies, the agent continues, even if it is waiting for some action of the dead partner.

Completed work continued
Completed Work continued

Built ALFworld – a 40 node Beowulf

Plan for today
Plan for Today

  • Sketch of artificial world - situated and mobile agents

  • Take a look at a few perception functions – congestion, reliability, difference

  • Conclude with 2003 Activities

World architecture
World Architecture

AutoPilot Network Components

Certificate Authority


World Places


Docked AutoPilot

Service Places

Docking Agent

Service Planner



Trader Places

Situated agents

Docking Agentcertifies arriving AutoPilots are legitimate in network and at site

Service Plannerreceives request from AutoPilot, requests service locations from Trader based upon AutoPilot passport and security clearance; returns service advertisements to AutoPilot.

Situated Agents

Certificate Authority





Docking Agent


Service Planner


Service Planneralsorequests geodesics from Topologist and computes Cost of Service to advertise in next Trader update to indirectly manage agent arrival rates.

  • Annihilatorkills off rogue processes and is last resort for population control

  • Topologistinterfaces to network router and requests current shortest path lengths to Service Places being considered by AutoPilot

Certificate Authority





Docking Agent

Service Planner



  • Trader requests environmental and service statistics from Service Places and updates advertisements.

  • Tradercertifies validity of Service Place requests for advertisement.

    If Traders are multiple or hierarchical, Trader also implements the Trader to Trader security protocol.

Certificate Authority





Docking Agent

Service Planner



Autopilots are mobile autonomous agents
AutoPilots are Mobile, Autonomous Agents

  • AutoPilot picks up payload consisting of signature, metadata and preferences from birth location.

  • Docking Agent obtains passport and security clearance from Certificate Authority.

  • Autopilot requests for each service in its signature, a set of advertisements from the Service Planner and may prune them. Autopilot returns a set of acceptable advertisements and attribute preference set to Service Planner.

Certificate Authority





Service Place

Docking Agent

Service Planner


Trader Places


  • Service Planner computes attribute values for each Service Place.

  • AutoPilot reasons next site based upon perception of attribute values returned by Service Planner.

  • After last service executed, AutoPilot returns payload and meta data to originating Docking Agent at birth location.

  • Annihilatorterminates AutoPilot and reports returns Passport to Certificate Authority.

Certificate Authority





Docking Agent

Service Planner



Perception by characteristic function

Perception by Characteristic Function

Congestion, Reliability, Difference

Agent framework
Agent Framework

  • To discriminate between service locations in a network, an agent must be able to sense its environment.

  • Since all nodes exhibit all attributes, agent preference must govern perception. We perceive local conditions by characteristic functions applied to sensed values.

  • Reasoning is “fuzzy” being approximate and over uncertain and aged data.

  • Behavior is limited to non-deterministic choice of service and migration

  • Meta data provides the context for decision making

Meta data





Autonomous, Rational Agent

Perception function goals
Perception Function Goals

  • Functions should produce “reasonable” output

  • Perceptions should have good correspondence to the subjective notions they represent

  • Functions should be based in theory, i.e. a characteristic function with PDF and CDF over the universe values of the attribute

Perception occurs in subjective time
Perception occurs in subjective time

  • An Agent’s life is finite in the system

  • An Agent carries a task signature yielding an expectation of the duration of work

  • An Agent must “sense” its own mortality in subjective time by half life i.e. sense urgency

where N0 and NT are initial and decayed

attribute values respectively

Role of origin and limit in perception functions
Role of Origin and Limit in Perception Functions

  • In subjective time, the origin corresponds to the agent’s threshold of sensitivity to the service attribute.

  • In subjective time, the limit corresponds to the agent’s tolerance, i.e. value of indifference for the attribute, beyond which all values are unacceptable.

  • These two values set the slope over which the perception function is differentiated in order to return a fuzzy membership value.

Perceiving expected waiting time
Perceiving Expected Waiting Time

  • The congestion function is F(x)=r/m(1-r)

Waiting Time


Service place population first order approximation
Service Place Population First Order Approximation

  • let l be arrivals per unit of time and m be services per unit of time.

The case for congestion set membership
The Case for Congestion Set Membership

  • Simply computing a wait time is not sufficient because it is without subjective value to the agent

    • there may be no wait time below the desired delay contribution

    • the wait time and utilization alone are insufficient to determine whether a node is in an “unsafe” state

    • any node not idle is “congested”

  • “Unsafe state ” is a condition subject to the Agent’s tolerence,i.e., corresponding to the attribute limit for the sensed value

Differentiating rate of change demands an origin and limit
Differentiating Rate of Change demands an origin and limit

  • The rate of change for the exponential function is constant over any interval.

  • The point at which the derivative of the function matches the average rate of change is the congestion point for the Service Place Utilization.

  • Average rate of change is of the form f’(x) = f(b)-f(a)/b-a where a,b are the origin and limit respectively.

Unsafe states and subjective time
Unsafe States and Subjective Time

  • If an Agent has a priority task or is aged relative to its remaining work, its tolerance for delay (r) decreases

  • This tolerance though subjective, is always bounded 0  origin  perceived value  limit 1.

  • To find this point in the congestion function requires we compute rho (r) [on the x axis] and waiting time W(r) [on the y axis].

Finding the subjective unsafe states
Finding the Subjective Unsafe States

These equations give the Waiting Time and Service Place

Utilization values above which Service Places are “unsafe” with respect to the agent’s perception of time.

Service place membership in the fuzzy set congestion r l q
Service Place Membership In The Fuzzy Set Congestion r(Lq)

  • Suppose at inquiry the Trader reported [SPname, Lq,μ,λ].

Validation of perception function
Validation of Perception Function

  • Extreme Valued Test Case

    List of Service Places at time of Trader Update were

    arbitrarily configured with some nodes congested

    SpID qLength (ServiceRate) (ArrivalRate)

    1 0 5.1 7.5

    2 1 5.1 7.5

    3 2 5.1 7.5

    4 3 5.1 7.5

    5 4 5.1 7.5

    695 5.1 7.5

    797 5.1 7.5

    899 5.1 7.5

    9101 5.1 7.5

    10103 5.1 7.5

Output of perception function
Output of Perception Function

Service Places

Configured as Congested

List of Agents

AgentID Tolerance SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8SP9 SP10

1 0.500000 --- *** *** *** *** *** *** *** *** ***

2 0.800000 --- *** *** *** *** *** *** *** *** ***

3 0.900000 --- --- *** *** *** *** *** *** *** ***

4 0.940000 --- --- --- *** *** *** *** *** *** ***

5 0.970000 --- --- --- --- *** *** *** *** *** ***

6 0.980000 --- --- --- --- --- *** *** *** *** ***

7 0.990000 --- --- --- --- --- *** *** *** *** ***

8 0.999000 --- --- --- --- --- *** *** *** *** ***

9 0.999900 --- --- --- --- --- *** *** *** *** ***

10 0.999990 --- --- --- --- --- --- --- --- --- ---


*** indicates Service Places perceived as congested

--- indicates Service Places not perceived as congested


  • Service Places 6 to 10 should be considered congested by all agents since they were configured in that condition.

  • Agents 1 - 9 did perceive Service Places 6 to 10 as congested.

  • Agents 1 to 5 were to conservative according to their tolerance for delay

  • Agent 10 was greedy according to its lack of constraint on delay.

Perceiving reliability again note need for origin and limit values
Perceiving Reliabilityagain note need for origin and limit values

The bath tub function
The Bath Tub Function

General definition of the bathtub-function

f(x) = β*x(β-1)*e(α1*x)[1]

This is a special combination of the Weibull rate and the log linear rate.

β is a shaping parameter, which is responsible for the curve and α1 is a nuisance parameter for fine-tuning the curve (it “controls” the Wear-Out-Phase).

Derivative of the Bathtub-function

f΄(x) = β2*xβ–2 *eα1*x- β*xb-2*eα1*x + β *xβ-1* α1*eα1*x [2]

Mortality sensing requirements
Mortality Sensing Requirements

  • The agent must know its chronological age since birth

  • The agent must know its current half life and (origin,limit) in order to determine its “indifference time” from the mortality function i.e. at the limit all solutions are unacceptable. Origin may be set to zero.

  • The agent must know αiβi from the Trader for the SP or service being evaluated. These are provided from the Sponsor’s empirical experience.

Perceiving mortality
Perceiving Mortality

  • mortality age

    y = βix(βi-1)*e(αix)

  • membership in the fuzzy failure set is determined

Mortality should be contrasted to the stability of the entity s mortality age
Mortality should be contrasted to the stability of the entity’s mortality age

  • f’(x) is a kind of stability factor. It indicates the amount of change of the expected mortality, which might be important if the agent is interested in a long-term cooperation or is risk averse. A positive value will indicate an increasing risk, a negative value a decreasing risk.

Stability of the mortality age relative to the entity is given
Stability of the mortality age relative to the entity is given

f’(x) =βi2xβ i–2 *eαix-βi*xbi-2*eαix +


Sample output

Age : 1.0 given

AgentMortalityValue: 0.60

EntityMortality : 0.51

FailureSetMembership : 0.86

Stability : -0.18

Age : 1.5

AgentMortalityValue: 0.60

EntityMortality : 0.46

FailureSetMembership : 0.76

Stability : -0.07

Sample Output

Perceiving difference
Perceiving Difference given

  • The natural log function can be useful in assessing “real differences” between attributes or entities

    f(x) = log x [1]

    f΄(x) = 1/x [2]

    x = e y (inverse) [3]

We still need origin and limit
We still need origin and limit given

  • Let origin = 0, limit be the point of indifference

  • Let x be the sensed value

  • Let y be the preference value

  • To determine the “perceived difference” between sensed value and preferred value dx = ey , i.e. transform y to x not x to y

Compute membership value and stability of perceived value
Compute membership value and stability of perceived value given

  • fuzzy membership in the “difference” set is x/dx.

  • stability of the perception is 1/x

    XValue = servicePlace->xValue;

    YValue = logf(XValue);

    AgentYValue = this->maxYValue;

    AgentXValue = exp(AgentYValue);

    StrengthConsiderability = XValue/ AgentXValue;

    if (StrengthConsiderability>1)StrengthConsiderability=1;

Sample perceptions
Sample Perceptions given


XValue: 1.50

YValue: 0.41

AgentYValue: 1.00

AgentXValue: 2.72


Stability: 0.67


XValue: 2.00

YValue: 0.69

AgentYValue: 1.00

AgentXValue: 2.72


Stability: 0.50


XValue: 3.00

YValue: 1.10

AgentYValue: 1.00

AgentXValue: 2.72


Stability: 0.33

2003 activities
2003 Activities given

  • Select method of fuzzy reasoning for mobility decision and validate

  • Determine COS function using first order queuing approximations

  • Implement essential ALFworld agents and Trader Update policy

  • Begin work on design pattern

  • Begin work on genetic representation of mobile agent