KAIST CS780 Topics in Interactive Computer Graphics : Crowd Simulation A Task Definition Language for Virtual Agents WSCG’03 Spyros Vosinakis, Themis Panayiotopoulos

KAISTKAISTKAIST CS780 Topics in Interactive Computer Graphics : Crowd Simulation

A Task Definition Language A Task Definition Language for Virtual Agentsfor Virtual Agents

WSCG’03Spyros Vosinakis, Themis Panayiotopoulos

Informatics Dept., University of Piraeus

Presenter : Sie-kyung Jang, VR Lab., KAIST


OutlineOutline

• Introduction• Approach• Related Work• 3D Environment with Virtual Agent• The Task Definition Language

– Actions– Arguments– Defining a Task

• Example• Conclusion


Introduction (1/2)Introduction (1/2)

• Virtual Environment as a user interface can be important for certain type

• Virtual Environment is more attractive when they are populated by virtual agents– Virtual Agent

• Autonomous entity in a virtual environment• Look like and behave as a living organism• To enhance virtual agent’s autonomy model the agent’s

functionality and behavior so as to resemble the real one


Introduction (2/2)Introduction (2/2)

• Strong dependence between the action definition and context

• Lack of general-purpose tools for designing and implementing intelligent virtual environment

• No tool to describe action combinations and sequences needed to achieve specific tasks


ApproachApproach

• Lack of standard architectures, methodologies and general-purpose tools Context-independent task definition

• No tool to define action combination and sequences needed to achieve specific tasks Use of a procedural language

• Context-independent definition tasks using a high-level language


Related WorkRelated Work

• Task execution of agents is an important issue

• Three Important Approaches– Parameterized Action Representation [Bad00]– Smart Object approach [Kal02]– Improv system [Per96]


3D Environment with Virtual 3D Environment with Virtual AgentAgent

• SimHuman – Tool for creation of 3D Environments with virtual agents

– Consists of a programming library• Embedded characteristics : Inverse Kinematics, Physically

Based Modeling, Collision Detection, Response, Vision• Define and animate 3D scenes with an arbitrary number of

objects and virtual agents

– Consists of two utilities• Designing the environments• Designing the agents’ animation sequences


3D Environment with Virtual 3D Environment with Virtual AgentAgent

• SimHuman– Entities

• Type – Agent : Perform actions and perceive the current state Have an autonomous operation– Object : World, …

• Tree-structures hierarchy• Geometry• A set of attributes

– <name, type, value>


The Task Definition LanguageThe Task Definition Language

• Fill the gap between higher-level decision processes and the agent’s motion and interaction in the environment

• Combine numerous built-in functions and commands to describe complex task

• Advantages – Easier for the user to specify action combinations and

scenarios for virtual agents– Easily reuse task with different agents , environments


Task Structure Task Structure

• Structure

<task Definition>

#Variables< variable declaration>

#Body<block of commands>


ActionsActions

• Process that causes changes to the world , entities • Has duration (Fixed or variant)• Executed in continuous timeframes

• Primitive action– Set of commands that an agent can perform in one

timeframe– SimHuman

• Changing the geometric properties of entities• Adding / removing entities to / from the world• Sending messages to other agents

– Agent can execute more than one primitive actions in one timeframes


Actions RepresentationActions Representation

• Implemented as a sequence of primitive action sets

• Predefined– a1, a2, … ,an with duration = n *∆t

– ai : set of primitive actions performed in timeframe I

• Goal-oriented– ai = f(ai-1, I, G)

– I : Set of info. About the object & properties the agent receives

– G : Goal


ActionsActions

• Keyframing– Ability to execute predefined animation sequences– Selecting the agent’s body parts and adjusting their

rotation

• Animation library– Set of user defined animation sequences– Anim <name>


ActionsActions

• Locomotion– Ability to walk inside the environment– Use state machine

• Ensure that the movement and rotation of the body takes place in a correct and believable manner

• Inverse Kinematics– IK <chain> <position>– Continuous correction sequence

• Tests at every step the best rotation for each joint to achieve the target

– Works with moving targets, avoid collision


Arguments – Variable Arguments – Variable

• Variable type – {boolean, integer, float, string, entity, list of entities, vector, list of vectors, relation}- relation : composition type

ex) person(‘John’, 28, 1.80, ‘single’)

• Variable set Types– Agent’s attributes– Task arguments– Task’s internal variables– Other entity's attributes

• [<entity name>] <variable name>


Arguments – functionArguments – function

• Use of function as arguments allow actions to be called with values that are adapted to different environments

• One can define tasks that may be executed in dynamic world

• Track the current state of the world, possible relations between entities.


Arguments – functionArguments – function

• Some functions detect spatial relations– Conditional execution of actions– Managing the agents beliefs about the world– Using current geometric properties (position, size,

orientation)– Evaluated by current geometric properties

(position, size, orientation)– Near, on, front_of, behind, left_of, right_of, above, below

• Some function deal with logical properties– Useful for defining complex conditions– and, or, not


Task StructureTask Structure

• Structure

<task Definition>

#Variables< variable declaration>

#Body<block of commands>

-c1; c2; … ci : ci is task commands

Possible Commands‐ <action>‐ PAR(<block b1>, <block b2>)‐ DO(<block b>) UNTIL c‐ IF <bool c> THEN (block b1>) ELSE (<block b2>)

- TASK name (type1 arg1,.., typen argn)

- Defines local variables


Interacting with ObjectInteracting with Object

• Catch an object using hand

< Doing without grasping >– End-effecter on the surface of the hand – Spot on the surface of the object– Use inverse kinematics action

– Suitable for large environments that need simplified agent models.

Example


Interacting with ObjectInteracting with Object

< Doing with grasping > Need complex agent models and skeleton

1) Define many spots • Use IK motions using PAR command

2) Constantly rotate all fingers• Use DO-UNTIL command• Avoids the definition of spots

More general• Must use of constant collision detection checks

increase the computational cost

Example


Observing the EnvironmentObserving the Environment

• Observing is needed when agent do not know which object to interact with

Example


A complete ScenarioA complete Scenario

• Agent visiting a bar– walking around until there is a

free table– Sitting on a chair– Calling a waiter– Ordering drink– Drinking from a bottle

• Decrease every time the agent brings the bottle to the mouth

– Communication between agent and waiter


ConclusionConclusion

• Context-independent definition tasks using a high-level language

• Future Work– Addition of more complex object interactions

( Facial animation for expressing the agents’ emotions )– Improve action execution and the world functionality

(Able to add more bio-mechanical characteristics to the agent)

Documents

KAIST CS780 Topics in Interactive Computer Graphics : Crowd Simulation A Task Definition Language for Virtual Agents WSCG’03 Spyros Vosinakis, Themis Panayiotopoulos