Integrating social skills in task-oriented 3D IVA Fran Grimaldo, Miguel Lozano, Fernando Barber, Juan M. Orduña Departament of Computer Science - University of Valencia (Spain) IVA 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 Intelligent Virtual Agents 2005, Kos (Greece) 3D IVA Spectrum Reactive 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 Intelligent Virtual Agents 2005, Kos (Greece) Integration of social mechanisms (coordination) to enrich the agent-centered decision making Inhabited Intelligent Virtual Environments COORDINATION Tuple centers Interaction 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) COMMUNICATION Agent Communication Languages: KQML vs FIPA ACLCOOPERATION Group decision making governed 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. Intelligent Virtual Agents 2005, Kos (Greece)

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. Intelligent Virtual Agents 2005, Kos (Greece)

Social Model: System architecture Intelligent Virtual Agents 2005, Kos (Greece) 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) Intelligent Virtual Agents 2005, Kos (Greece)

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. Intelligent Virtual Agents 2005, Kos (Greece)

Social Model: Task Coordination Approach 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) Intelligent Virtual Agents 2005, Kos (Greece)

Social Model: Task Coordination Intelligent Virtual Agents 2005, Kos (Greece) Approach 2: Goal Partitioning Weight the heuristic (cost estimation to achieve each fact contained in the agents’ goal) depending on their procedence. TEAMFORMATIONTEAMFORMATION Book3 on Book2 Book2 on Book1 Book1 on Table3 Book3 on Book2 Book2 on Book1 Book1 on Table3 Plant1 on Table1 Plant2 on Table2 AGENT A AGENT B Book3 on Book2 Book2 on Book1 Book1 on Table3 Plant1 on Table1 Plant2 on Table2 AGENT B Plant1 on Table1 Plant2 on Table2 AGENT A Book3 on Book2 Book2 on Book1 Book1 on Table3

Social Model: Task Coordination Intelligent Virtual Agents 2005, Kos (Greece) Approach 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 (h add - Geffner) Plant1 on Table1  0.2 Plant2 on Table2  0.2 AGENT A Book3 on Book2  1 Book2 on Book1  1 Book1 on Table3  1

Results Intelligent Virtual Agents 2005, Kos (Greece) Non-Communicative agents

Results Approach 1: Communicative agents Intelligent Virtual Agents 2005, Kos (Greece)

Results Room organization without coordination Intelligent Virtual Agents 2005, Kos (Greece)

Results Approach 1 (Non Conversational) + Aproach 2  Task Coordination Intelligent Virtual Agents 2005, Kos (Greece)

Results Intelligent Virtual Agents 2005, Kos (Greece) ProblemAgentTasksInterruptionsInteractions (Average) Interactions (Max) Independent Goals No Coordination A B C Independent Goals Coordinated W[ext] = 1 A7111 B C8411 Independent Goals Goal Partitioning W[ext] = 0.2 A4011 B5111 C601.22

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) Intelligent Virtual Agents 2005, Kos (Greece)

Integrating social skills in task-oriented 3D IVA Fran Grimaldo, Miguel Lozano, Fernando Barber, Juan M. Orduña Departament of Computer Science - University of Valencia (Spain) IVA 2005, Kos (Greece)