Unified theories of cognition
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Cognitive modeling: knowledge, reasoning and planning for intelligent characters
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Dynamically altering agent behaviors using natural language instructions
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
HPTS: a behaviour modelling language for autonomous agents
Proceedings of the fifth international conference on Autonomous agents
What Sort of Control System Is Able to Have a Personality?
Creating Personalities for Synthetic Actors, Towards Autonomous Personality Agents
Towards Personalities for Animated Agents with Reactive and Planning Behaviors
Creating Personalities for Synthetic Actors, Towards Autonomous Personality Agents
Sensor Based Synthetic Actors in a Tennis Game Simulation
CGI '97 Proceedings of the 1997 Conference on Computer Graphics International
Dynamic Control of Intention Priorities of Human-like Agents
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
An agent's activities are controlled by his priorities
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Activity scheduling for a robotic caretaker agent for the elderly
International Journal of Intelligent Information and Database Systems
Synthesis of concurrent object manipulation tasks
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
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Reproducing daily behaviours requires the ability to schedule behaviours depending on resources (body parts for example) and priority (intentions or physiological parameters) constraints. A simple way is to say that behaviours which are using the same resources are mutually exclusive. This approach is not sufficient to achieve realism purpose, as in real life, humans are able to combine them in a much microscopic way. All day long, humans mix different behaviours, as for example reading a newspaper while drinking a coffee and smoking a cigarette. If all behaviours using common resources were mutually exclusive, an agent could not reproduce this example, except if a specific behaviour is created. This solution becomes rapidly too complex and has motivated the work presented in this paper. It consists in an extension of HPTS, our behavioural model, by the introduction of resources and priority levels. In the contrary of some previous approaches, it is not necessary to specify exhaustively all behaviours that are mutually exclusive; this is done implicitely by attaching resources to nodes and a priority function to each state machine, and by using a scheduler.