Integrating social skills in task-oriented 3D IVA Fran Grimaldo, Miguel Lozano, Fernando Barber, Juan M. Orduña Departament of Computer Science - University

Integrating social skills in task-oriented 3D IVA

Fran Grimaldo, Miguel Lozano, Fernando Barber, Juan M. OrduñaDepartament of Computer Science - University of Valencia (Spain)

[email protected] 2005, Kos (Greece)

Outline• Introduction

• Communication–Coordination–Cooperation

• Requirements for social task-oriented 3DIVA

• Social Model– Grouping– Task coordination

• Results

• Conclusions and Future Work

Intelligent Virtual Agents 2005, Kos (Greece)

Introduction


3D IVA SpectrumReactive agents

Deliberative agents

Simple individuals with limited operation (e.g. movement)

From classical boids (Reynols) up to more complex crowds

Actors that are able to execute

complicated tasks

Jack or Steve do not exactly

perform p2p collaboration

Interactive storytelling and

group simulation scenarios

Global approaches with somehow

predefined interactions

Communication – Coordination – Cooperation


Integration of social mechanisms (coordination) to enrich the agent-centered decision making

Inhabited Intelligent Virtual Environments

COORDINATIONCOORDINATION

Tuple centersInteraction Protocols

ACL Semantics

Ex: MAPL or GPGP

A number of task-oriented agents facing non decomposable problems easilly falls in conflictive situations even though their goals are compatible (obstruction)

COMMUNICATIONCOMMUNICATION

Agent CommunicationLanguages:

KQML vs FIPA ACL

COOPERATIONCOOPERATIONGroup decision makinggoverned by a leader

(Ex: STEAM)vs

Distributed planning(Ex: SharedPlans)

Requirements for social task-oriented 3DIVA

• Agents in shared environments need to feed their planning system to overcome their lack of information Animation of dialogues.

• Interaction situations: resource competition, grouping between actors and joint task execution.

• Grouping between actors:– Commitment to a goal (JIT).– Persistent vs Ephemeral associations.– When to create and destroy teams.– Aim: Avoid obstruction between the members.


Requirements for social task-oriented 3DIVA

• Task coordination:– Def: Managing dependencies between activities.– Complete perceived state with the intentions of the other

characters Pre-planning coordination.– Approach 1: Shared resources.

• Include activities already planned by the others.• Aim: Reduce resource interference (Avoid using busy objects).

– Approach 2: Goal Partitioning.• Include goals of the teammates in the planning process.• Divide the total goal (own and external facts) in a set of “near

independent" subgoals looking at the objects used by each fact.• Weight the heuristic of each fact in the global goal.• Aim: Reduce interferences between activities.


Social Model: System architecture


• Multi-agent animation system with distributed architecture.• Communication model throughout the world (sense/act).• Environment delivers ACL messages between actors that

animate conversations thanks to their Task Controller.

Social Model: Conversational Task Controller

When to start a conversation? Under task interruption(external interference)


Social Model: Memory

• Represent the operation of external agents in the memory.

• Cooperation as an intentional internal posture.

• Communication Belief (C_Belief): Information about the ongoing tasks.


Social Model: Task CoordinationApproach 1: Shared ResourcesApproach 1: Shared Resources

• Trusting new c_beliefs:– HSP with STRIPS operators No timing– Check compatibility of the new information– Lock resources being used (Mark signal)

• Removal of c_beliefs from the memory:– Precondition checking over perceived state.– Invalidate signal.

• How HSP planner uses c_beliefs:– Start the search from a future virtual state– Avoid using busy objects at the first level of the search.– Pre-Planning coordination (interleaved)


Social Model: Task Coordination


Approach 2: Goal PartitioningApproach 2: Goal Partitioning• Weight the heuristic (cost estimation to achieve each fact contained in the

agents’ goal) depending on their procedence.

TEAM

FORMATION

Book3 on Book2Book2 on Book1Book1 on Table3


Plant1 on Table1

Plant2 on Table2

AG

EN

T A

AG

EN

T B


Plant1 on Table1

Plant2 on Table2

AG

EN

T B

Plant1 on Table1

Plant2 on Table2

AGENT A


Social Model: Task Coordination


Approach 2: Goal PartitioningApproach 2: Goal Partitioning• W[own]: Weight for the internal facts = 1• W[ext]: Weight for the external facts• Give preference to the own goals (W[ext] < 1) • Example: W[ext] = 0.2

• Heuristic: Distance to the goal (hadd - Geffner)

Plant1 on Table1 0.2

Plant2 on Table2 0.2

AG

EN

T A Book3 on Book2 1

Book2 on Book1 1Book1 on Table3 1

Results


• Non-Communicative agents

Results• Approach 1: Communicative agents


Results• Room organization without coordination


Results• Approach 1 (Non Conversational) + Aproach 2 Task Coordination


Results


Problem Agent Tasks Interruptions Interactions(Average)

Interactions (Max)

Independent GoalsNo Coordination

A 10 3 1.4 2

B 15 7 1.6 2

C 12 3 1.5 2Independent GoalsCoordinatedW[ext] = 1

A 7 1 1 1

B 5 1 1.3 2

C 8 4 1 1Independent GoalsGoal PartitioningW[ext] = 0.2

A 4 0 1 1

B 5 1 1 1

C 6 0 1.2 2

Conclusions and Future Work• The task-oriented 3DIVA presented incorporate

social information into the agent-centered decision making in order to reduce interferences, resolve conflicts to finally enhance behavioral animation.

• Grouping and task coordination helps to manage social behaviours in shared scenarios.

• Test the model over more complex scenarios.• Include more sophisticated interactions between

actors (e.g. ask someone to do something)


Integrating social skills in task-oriented 3D IVA

Fran Grimaldo, Miguel Lozano, Fernando Barber, Juan M. OrduñaDepartament of Computer Science - University of Valencia (Spain)

[email protected] 2005, Kos (Greece)

Documents

Integrating social skills in task-oriented 3D IVA Fran Grimaldo, Miguel Lozano, Fernando Barber, Juan M. Orduña Departament of Computer Science - University