A Systematic View of Agent-supported Simulation

Past, Present, and Promising Future

Tuncer Ören

, Levent Yilmaz

and Nasser Ghasem-Aghaee

3,4

School of Electrical Eng. and Computer Science, Univ. of Ottawa, Ottawa, ON, Canada,

Computer Science and Software Engineering, Samuel Ginn College of Engineering, Auburn University,

Auburn, AL, U.S.A.

Computer Eng. Dept. of Sheikh Bahaei Univ., Baharestan, Iran

Univ. of Isfahan, Isfahan, iran

Keywords: Agent-directed Simulation, Simulation for Agents, Agent Simulation, Software Agents for Simulation,

Agent-supported Simulation, Agent-based Simulation, Agent-monitored Simulation, Agent-triggered Simu-

lation.

Abstract: Agent-supported simulation involves the use of intelligent agents to enhance modeling and simulation

(M&S) infrastructures and consists of support by software agents: (1) to provide computer assistance for

front-end and/or backend interface functions in M&S environments; (2) to process elements of an M&S

study symbolically (for example, for consistency checks and built-in reliability); and (3) to provide cogni-

tive abilities to the elements of an M&S study, such as perception, anticipation, learning or understanding

abilities. Several aspects of agent-supported simulation are clarified and references are provided.

1 INTRODUCTION

Agent-supported simulation is a special case of

agent-directed simulation and involves the use of

intelligent agents to enhance modeling and simu-

lation (M&S) infrastructures. Agent-supported simu-

lation involves use of software agents: (1) to provide

computer assistance for front-end and/or backend

interface functions in M&S environments; (2) to

process elements of M&S studies symbol-lically (for

example, for consistency checks and built-in reliabil-

ity); and (3) to provide cognitive abilities to the ele-

ments of M&S studies, such as learning or

understanding abilities.

Section 2 is a very brief overview of simulation

and software agents. In section 3, we elaborate on

synergies of simulation and agents to provide an

appropriate perspective to conceive properly agent-

supported simulation. In section 4, we focus and

elaborate on several aspects of agent-suppor-ted

simulation. Section 5 is a review of past and present

realizations of agent-supported simulation as well as

promising development areas. Section 6 includes

conclusions and some future activities. Due to space

limitations, only main aspects and references are

given.

2 BACKGROUND

2.1 Simulation

Two aspects of simulation, i.e., experimentation and

experience need to be emphasised for the scope of

this article. So far as its experimentation aspect is

concerned, simulation is performing goal-directed

experiments with models of dynamic systems. So far

as its experience aspect is concerned, (1) simulation

is providing experience under controlled conditions

for training, i.e., for gaining/enhancing competence

in one of the three types of skills: (i) motor skills

(virtual simulation), (ii) decision and/or communica-

tion skills (constructive simulation; serious game),

and (iii) operational skills (live simulation) or (2)

simulation is providing experience for entertainment

purpose (gaming simulation). For further details, see

Ören (2011a, b).

2.2 Software Agents

Software agents are autonomous software modules

with perception and social ability to perform goal-

directed knowledge processing over time, on behalf

of humans or other agents in software and physical

497

Ören T., Yilmaz L. and Ghasem-Aghaee N..

A Systematic View of Agent-supported Simulation - Past, Present, and Promising Future.

DOI: 10.5220/0005138804970506

In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),

pages 497-506

ISBN: 978-989-758-038-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

environments. When agents operate in physical envi-

ronments, they can be used in the implementation of

intelligent machines and intelligent systems and can

interact with their environment by sensors and effec-

tors. The core knowledge processing abilities of

agents include: goal-directed knowledge processing,

reasoning, planning, motivation, and decision mak-

ing. The factors that can affect decision making abil-

ities, such as personality, emotions, and cultural

backgrounds can also be embedded in agents.

Agents may have additional abilities such as under-

standing, including understanding and expressing

emotions, awareness, as well as ethical behavior.

3 SYNERGIES OF SIMULATION

AND AGENTS

3.1 Agent-directed Simulation

Agent-directed simulation refers to the synergy of

software agents and simulation. As shown in Figure

1, there are three categories of possibilities that can

be considered under two groups: (1) contribution of

simulation to agents: which consists of agent simula-

tion and (2) contribution of agents to simulation

which consists of agent-supported simulation and

agent-based simulation (Ören, 2001a; Yilmaz and

Ören, 2009):

• Agent simulation is simulation of agent systems or

simulation of systems modeled by using software

agents.

• Agent-supported simulation is use of agents for at

least one of the following purposes:

(1) to provide agent assistance for front-end inter-

face functions in a computer-aided modeling

and simulation study;

(2) to provide agent assistance for back-end inter-

face functions in a computer-aided simulation

study;

(3) for symbolic processing of elements of a simu-

lation study –for consistency checks, for exam-

ple; and

(4) to provide cognitive abilities to the elements of

a simulation study –such as learning, under-

standing and/or hypothesis formulation.

• Agent-based simulation is use of agents for the

generation and/or monitoring of agent behavior.

(This is similar to the use of AI techniques –like

qualitative simulation– for the generation of model

behavior).

Figure 1: Three categories of Agent-directed Simulation.

3.2 Agent Simulation

Agents provide a natural paradigm to represent intel-

ligent entities. Agent simulation is simulation of nat-

ural or engineered entities represented by agents

(Yilmaz and Ören, 2009).

3.3 Agent-based Simulation

Agent-based simulation is the use of software agents

during run time to monitor and generate model-

behavior. This is similar to the use of AI techniques

for the generation of model behavior, e.g., qualita-

tive simulation and knowledge-based simulation.

The possibilities include agent-triggered simulation,

agent-monitored simulation; agent monitored cou-

plings, agent-monitored multi-model transitions…

The term agent-based simulation is also used to

mean agent simulation when the two other possibili-

ties of contribution of agents to simulation are not

taken into consideration

4 AGENT-SUPPORTED

SIMULATION

Agent-supported simulationwhich is the focus of

Contribution of simulation to agents

Contribution of agents to simulation

Agent simulation

or simulation of agent systems

Agent-supported simulation

Agents are used to:

- support front-end interfaces

- support back-end interfaces

- process symbolically elements of

simulation

- assure cognitive abilities to the

elements of simulation systems

Agent-based simulation

Agents are used during run-time for

the generation or monitoring of mod-

el behavior

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this article is the use of agent technology to support

simulation activities in modeling and simulation

environments as well as simulation-based problem

solving environments (or simulative problem solving

environments).

The possibilities for agent support in modeling

and simulation environments are outlined in Table 1.

The agent support can be for the following elements

of simulation studies: goal of the study, parametric

models, model parameters, design of experiments,

experimental conditions, simulation program, and

the behavior and recommendations. For each ele-

ment, the support can be for the generation, specifi-

cation/editing, as well as processing of the element.

For each category of knowledge and knowledge pro-

cessing knowledge (Ören 1990), an important cate-

gory of knowledge processing activity is to ensure

the reliability of the associated elements.

Table 1: Possibilities for agent support in modeling and

simulation environments.

For

For specification/ genera-

tion/editing

For processing

Goal

-goal specification /

editing

-goal generation

-hypothesis formulation

-goal processing

--goal seeking

--modification

--evaluation

--selection

Param-

etric

model

-modelling

--model composition

--model editing

-model-base

management

-model analysis

--characterization

--evaluation

-model

transformation

Model

param-

eters

-parameter estimation/

calibration

-editing

--parameters

--auxiliary parameters

-symbolic

processing

--parameters

--auxiliary

parameters

Design

of expe-

ri-ments

- design/editing of

experiments

-processing of

design of

experiments

--evaluation

--selection

For

every

experi-

ment

-specification/editing

of experimental

conditions

--initial conditions of

state variables

--behavior generator

--behavior generation

parameters

-automatic

selection

--behavior

generator

--behavior

generation

parameters

-reliability

Simula-

tion pro-

gram

-transformation of

problem specifications

into a simulation

program

-automated editing of

simulation programs

- processing

sim. programs

(legacy programs,

new programs)

-program

understanding

-program reliability

4.1 Agent Support for Front-end

Interfaces

Table 2 outlines the front-end functionalities for the

elements of a modeling and simulation environment.

Front-end interface functionalities include: anticipa-

tion of user’s needs, help, just-in-time-learning, ex-

planation, awareness, assistance, guidance,

(un)solicited advice, advanced types of inputs such

as perception, speech input, body language, facial

expression, deictic input, and haptic input. Front-end

interface functionalities are applicable to goals, par-

ametric models, model parameters, experimentation

conditions, simulation pro-grams, and model behav-

iors.

Table 2: Some front-end interface functionalities.

4.2 Agent Support for Back-end

Interfaces

Back-end interfaces are used by systems to com-

municate to the users the primary and auxiliary out-

puts of the system. Table3 outlines the back-end

functionalities for the elements of modeling and

simulation environments. Back-end interface func-

tionalities provide support for behavior display, in-

strumenting/monitoring, processing, evaluation, and

advice. Advanced types of outputs such as augment-

ed/enhanced reality and virtual reality are part of the

Table 3: Some back-end interface functionalities.

- anticipation of user’s needs

- help formulate/specify problems

- awareness, just-in-time-learning, explanation

- assistance, guidance, (un)solicited advice

- abilities to process advanced types of inputs:

-- perception (focusing), speech input

-- body language, deictic input, haptic input

-primary outputs

--(un)processed behavior

--performance measure

--evaluation

--advice on the problem

-auxiliary outputs

--automated documentation

--explanation

with abilities to process advanced types of out-

puts such as:

--virtual reality,

--augmented reality

--holographic displays

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functionalities of back-end interfaces. Back-end in-

terface functionalities are applicable to behavior

displays, instrumenting, pro-cessing, evaluating,

explanation, and warning/advice.



4.3 Agent Support for Symbolic

Processing of Elements of

Simulation Studies

Intelligent agents can provide support in various

stages of the overall simulation development lifecy-

cle. For instance, in Model-Driven Engineering

(MDE) that involves automated transformation of

platform-independent abstract models, agents can

serve as transformation engines, by which increas-

ingly concrete and platform-dependent models and

simulations can be generated. Agents can also func-

tion as mediators and brokers for distinct simulations

by bridging the syntactic and semantic gap between

their representations. To support goal-directed ex-

perimentation, agents can bring transparency to the

overall experiment design (Ören 2001b), execution,

analysis, and adaptation process for various types of

experiments such as sensitivity analysis, variable

screening, understanding, optimization, and deci-

sion-support. Next, for illustrative purposes, we dis-

cuss these three application areas.

4.3.1 Agent-supported Model

Transformation

The common strategy in MDE is based on the appli-

cation of a sequence of transformations starting with

platform-independent models down to the concrete

realization of the simulation system. Besides the

reuse of models and deployment of designs in alter-

native platforms, MDE improves the reliability of

simulation systems through correctness preserving

transformations that allow substantiating the accura-

cy of realizations with respect to explicit constraints

and assumptions defined in the abstract models. To

facilitate the application of the MDE methodology

shown in Figure 2, models are defined in terms of an

explicit modeling language, which in turn is speci-

fied in terms of a meta-modeling language. The

transformations are executed by agents using a set of

rules, which are specified by using the constructs of

a specific transformation language

An agent with understanding capabilities as pre-

sented in (Ören et al., 2007) can be used to map con-

structs of a source meta-model to equivalent features

of the target meta-model. Such templates can be

customized and applied by agents upon models by

matching rules to constructs and elements of the

Figure 2: Model-Driven Engineering.

models. The strategies for defining such production

rules can vary depending on the sophistication of the

MDE environment. Transformation rules can be

produced by agents with understanding capabilities

from scratch or can be a refinement of a generic

specification template applicable to selected source

and target modeling languages. Alternatively, trans-

formation rules can be derived automatically out of

higher-level mappings rules between models. This

strategy requires (1) defining/discerning a mapping

of elements of one model to another model (e.g.,

model weaving) and (2) automating the generation

of the actual transformation through an agent inter-

preter or matcher that takes as input two model defi-

nitions and the mapping rules between them to

produce the concrete transformations.

4.3.2 Agent-supported Interoperability

The above mechanism can be extended (see Figure

3) to utilize agents to support interoperability. In

distributed simulation, interoperability refers to the

ability of system models or components to exchange

and interpret information in a consistent and mean-

ingful manner. This requires both syntactic and se-

mantic congruence between systems either through

standardization or mediators that can bridge the syn-

tactic and semantic gap between peer components.

Figure 3: Agent-supported Simulation Interoperability.

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Such mediator or bridge agents ensure both data-

level interoperability (i.e., metadata/data inter-

change) and operational-level interoperability (i.e.,

behavioral specification). Common strategies for

developing adapters involve the provision of stand-

ard APIs and connecting components through pub-

lished interfaces that explicitly specify the required

and provided services. This low-level connectors are

often limited and do not achieve full data interoper-

ability.

As shown in Figure 3, the use of agent-supported

transformation can provide a sound and comprehen-

sive framework for defining bridges. By making the

internal schema (i.e., meta-model) of each system

explicit and then aligning them via agents by match-

ing or weaving concepts, we can leverage model-to-

model transformations exploiting the matching in-

formation to export data conforming to the meta-

model of the target system or component. While

internal schema, structural specifications, and behav-

ioral models may be available along with the im-

plementation of the simulation system, in their

absence agents can also be supportive in deducing

such models for transformation. By extracting the

abstract syntax of the Platform-Specific Model of a

system and then transforming it into a set of PSMs

using agent transformation rules would be a first

step to automate the derivation of high-level specifi-

cations. Such specifications could then be used to

generate bridge rules in terms of model transfor-

mation language, which serves as the meta-model

for the translation rules that map the source da-

ta/behavioral specification to the target specification.

Such mapping rules can be used as bridge imple-

mentations in terms of mediator software agents.

4.3.3 Agent-supported Experimentation

An agent-coordinated support system could greatly

enhance the experimental design process in several

ways, but mainly by providing expert knowledge

that the user might lack (Ören, 2001). The agent can

decide which designs best fit the experiment’s objec-

tive, as well as which factors should be kept or dis-

carded after each iteration of the experiment’s life-

cycle. Concomitantly, the agent guides the process

by requesting the information it needs in order to

construct an experiment, verify the validity of the

user’s input and ensure the integrity of the experi-

ment elements.

As shown in Figure 4, the agent-supported Simu-

lation Experiment Management System (SEMS) is a

software tool that allows users to design, execute,

store, and share computer simulation experiments.

An ontology-assisted interface builder managed by

an interface agent that is aware of experiment ontol-

ogy guides the simulation experiment design. The

experiment design procedure is governed by a KEP-

LER Scientific Workflow process (Ludäscher et al.,

2006) that implements the experiment life-cycle.

Figure 4: Simulation Experiment Management System

Components (Data-Flow View).

At each step of the life-cycle, the user inputs, with

the help of an intelligent agent, the information re-

quired to construct an experiment. This experiment

structure is stored in an XML file that is used by a

synthesizer agent, which “translates” the XML

source into executable NIMROD (Abramson et al.,

1995) (experiment execution engine) code. Follow-

ing the execution of the experiment, the synthesizer

agent collects the results and forwards them to statis-

tical software to generate an XML representation of

the experimental results.

4.4 Agent Support to Provide Cognitive

Abilities to the Elements

of a Simulation

Software agents can provide cognitive abilities such

as perception (Ören, 2001), anticipation (Ören and

Yilmaz, 2012), and understanding (Ören, Ghasem-

Aghaee and Yilmaz, 2007) to the elements of simu-

lation studies. Table 11 includes some additional

possibilities.

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5 AGENT-SUPPORTED

SIMULATION: PAST,

PRESENT, AND FUTURE



5.1 Past and Present

Ándras Jávor did pioneering work by using “de-

mons” in modeling and simulation. In those early

days, even the term “software agent” was not yet

introduced in the scientific literature. (Jávor 1990,

1992; Jávor and

Szűcs, 1998). Another early con-

tributor to the field using demons was Hogeweg

(1979).

Tables 4 - 7 contain, respectively, samples of

references for agent-supported simulation front-end

interfaces, back-end interfaces, symbolic processing

of elements of simulation studies, and cognitive abil-

ities for the elements of simulation systems.

Table 4: Agent-supported simulation for front-end inter-

faces.

To support Author(s) Year

-airline ticket assistance Groves & Gini 2013

-animated interface agent Rist et al. 1997

-behaviour-based control Alexander et al. 2010

-collaborative interface

Eisenstein &

Rich

2002

Rich & Sidner 1997

-experimental design Ören 2001b

-intelligent interface

Pitts & Ping

Hwang

1999

Bikovska et. al. 2006

Tuchinda &

Knoblock

2004

-natural language interface Moran et al. 1997

-sensor/emitter model

Dryer 1997

Presser et al. 1999

-visualization environment Wasfy et al. 2004

Table 5: Agent-supported simulation for back-end inter-

faces.

To support Author(s) Year

-end-user individual-based

modeling

Ginot et al. 2002

-explanation

Haynes et.al. 2009

Vasconcelos et al. 2004

-natural language advice Kuhlmann et al. 2004

-route advice Rogers et al. 1999

Table 6: Agent-supported simulation for symbolic pro-

cessing.

To support Author(s) Year

-distributed symbolic

computation

Schimkat et al. 2000

-emergence of inquiry

conversation

Omori & Nishi-

zaki

1999

-simulation-based systems

engineering

Yilmaz & Ören 2010

-symbolic and behavioral

processing of data

Chella et al. 1997

-symbolic performance and

learning in continuous

valued environment

Rogers 1997

-test and refine models

Kennedy &

Theodoropoulos

2006

-verification and validation Balci 2004

Table 7: Agent-supported simulation for cognitive abilities

for the elements of simulation systems.

To support Author(s) Year

-adaptive elements Crain 1999

-adaptive mesh generation Hilaire et al. 2000

-agent decision making

Brouwers & Ver-

hagen

2003

-agent intelligence to support

human org.

Knoblock et al. 2008

-assessment model

Krywkow et al. 2002

-automated evaluation of

Internet business

Chong & Cho 2001

-cognitive emergence

Castelfranchi 1998

-cooperation tools for supply

chain management

Klingebiel et al. 2001

-decision assistant Itmi et al. 2002

-decision support Yilmaz & Tolk 2008

-design of experiments Ören 2001b

-dynamic reasoning Kazar et al. 2000

-help/documentation Fujishima 1997

-HLA-based distributed vir-

tual environments

Wang et al. 2003

-information warfare Mack & Alzone 1997

-integration of databases

using mobile code

Claro & Sobral 2000

-intelligent matchmaking for

information agents

Lu & Sterling 2000

-interoperation

Yilmaz & Paspu-

leti

2005

-mediation Novais et al. 2000

-multi-sensor planning Hodge & Kamel 2001

-office automation Thomas & Fischer 1997

-processes controlled by

agents

Kruzel & Vondrak 2000

-resource location on the

World Wide Web

Grey et al. 2000

-scheduling Pesenti et al. 2001

-selection recognition Pandit & Kalbag 1997

-social models Moss 1998

-traffic intersection control Dresner & Stone 2005

-understanding the design

requirements

Cohen et al. 1989

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5.2 Some Promising Research and

Development Areas

Some promising research and development (R&D)

areas

to fully benefit from the synergy of simulation and

agents are outlined in tables T8-11.

Table 8: Promising R&D areas: Front-end interfaces.

* Intelligent interface agents

--context and situation awareness

-- anticipation of user’s needs

---help formulate/specify problems

-- just-in-time-learning, explanation

--assistance, guidance, (un)solicited advice

* Abilities to process advanced types of inputs

--perception (focusing)

--natural language input

--body language interface

--emotional inputs

--deictic input, haptic input

--thought input

* Holographic avatars in front-ends

Table 9: Promising R&D areas: Back-end interfaces.

* Holographic avatars in back-ends

* Help to select solutions

* Clarification of solution

* Spoken output

Table 10: Promising R&D areas: Symbolic processing of

elements of simulation.

* Reliability of simulation studies and agents

--agent-based built-in reliability

--agent-based verification and validation

--agent-based failure avoidance

* Program generation from specifications

* Program integration

6 CONCLUSION

Software agents represent powerful computational as

well as modeling paradigms for autonomous entities.

In this article, we focused on agent supported simu-

lation and discussed the important benefits they

bring to modeling and simulation. We also elaborat-

ed on the past contributions, the state-of-the-art and

promising and important research and development

areas. We are planning to explore, in a sequel paper,

advanced possibilities of contributions of agents

during run time, i.e., of agent-based

simulation (e.g., agent-triggered simulation,

Table 11: Promising R&D areas: Cognitive abilities for

the elements of simulation systems.

* Cognitive abilities to the elements of simula-

tion, such as perception, anticipation, under-

standing, learning, and/or hypothesis

formulation

* Program understanding for documentation

and/or maintenance purposes

* Agents in simulation-based problem solving

environments

* Holons for goal-directed co-operation and col-

laboration (including “principled holons” who

can refuse certain types of cooperation)

* Simulation-based predictive displays for social

and financial systems:

--to train future policy/decision makers

--to predict abnormal deviations and

--to test and select possible corrective actions

* Simulation to test and evaluate autonomous

decisions by agents

* Agent-based ubiquitous (mobile) simulation

(including agent-based mobile cloud simula-

tion)

--selection of models

--selection of matching scenarios for experi-

mentation

agent-monitored simulation, agent-monitored dy-

namic coupling).

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