Towards Conscious-like Behavior in Computer Game Characters


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The main sources of inspiration for the design of more engaging synthetic characters are existing psychological models of human cognition. Usually, these models, and the associated Artificial Intelligence (AI) techniques, are based on partial aspects of the real complex systems involved in the generation of human-like behavior. Emotions, planning, learning, user modeling, set shifting, and attention mechanisms are some remarkable examples of features typically considered in isolation within classical AI control models. Artificial cognitive architectures aim at integrating many of these aspects together into effective control systems. However, the design of this sort of architectures is not straightforward. In this paper, we argue that current research efforts in the young field of Artificial Consciousness (AC) could contribute to tackle complexity and provide a useful framework for the design of more appealing synthetic characters. This hypothesis is illustrated with the application of a novel consciousness-based cognitive architecture to the development of a First Person Shooter video game character.

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Towards Conscious-like Behavior in Computer Game Characters

  1. 1. Towards Conscious-like Behavior in Computer Game Characters Raúl Arrabales, Agapito Ledezma and Araceli Sanchis g p Computer Science Department Carlos III University of Madrid htt //C i R b t /R l IEEE Symposium on Computational Intelligence and Games 2009 Politecnico di Milano, Milano, 9th September 2009
  2. 2. Contents Introduction Motivation and Objectives State of the Art Consciousness and Games Experimental Environment Proposed Architecture Evaluation Conclusions 2
  3. 3. Introduction (I) Objective: Design more appealing synthetic characters. mo e s nthetic cha acte s More engaging, more real. Human-like (Conscious-like behaviors). H lik (C i lik b h i ) Problem: Many cognitive s s involved. a y cog t e skills o ed How to integrate them effectively? 3
  4. 4. Introduction (II) Character design challenges: Emotions, planning, learning, opponent E ti l i l i t modeling, attention, set shifting, etc. How can they be integrated effectively? Our hypothesis: Use a cognitive architecture based on a model of consciousness. 4
  5. 5. Motivation and Objectives Ultimate goal: Characters Cha acte s able to pass the (adapted) Turing test. T ing test Why? State of the art video games g Complex virtual environments. Elaborated scripts. Require more realistic behaviors. 5
  6. 6. State of the art Current AI characters cannot match human- like behavior behavior. E.g. see last year 2K Botprize contest results. Usually, playing with other humans is more realistic and engaging that playing with AI bots. Apply Machine Consciousness to games to improve believability? 6
  7. 7. Consciousness and Games (I) Embodiment and situatedness are claimed to be critical for the production of consciousness. p We argue that consciousness based software consciousness-based agents can also be embodied and situated. Software sensors and actuators actuators. Causal interaction with the world. Virtual World (virtually situated). Real World (interaction with human players). 7
  8. 8. Consciousness and Games (II) Games versus Physical Robots Robots possess physical body. body Robots interact directly with physical world. Robots have to deal with noise and uncertainty. uncertainty These aspect can b seen as benefits or Th t be b fit drawbacks Games are ideal for focusing on h h l d lf f high-level control. l l 8
  9. 9. Experimental Environment FPS: Unreal Tournament 2004 Bot Behavior Controller Basic Behaviors B i B h i Sensory Data Processing Movement  Animation Module Module Sensory Body Inputs State Atomic Actions Virtual World 9
  10. 10. Proposed Architecture (I) Context: Machine Consciousness Research. Specifically: Design of machines showing conscious-like p y g g behavior. Access Consciousness (A C A C i A-Consciousness) Approach: i A h Only a small selection of contents gain access to a unique sequential thread for explicit processing and volition. q p p g Computational model based on: Global Workspace Theory (Baars, 1988). Multiple Draft Model (Dennett, 1991).
  11. 11. Proposed Architecture (II) Computational model of consciousness: Large number of specialized processors. L b f i li d Competing and collaborating to access a g global workspace (working memory). p ( g y) Where coherent information patterns are selected.
  12. 12. Proposed Architecture (III) Contexts Working Memory Focus of  Attention Specialized Processors Interim  Coalition
  13. 13. Proposed Architecture (IV) CERA-CRANIUM Architecture Implements the computational model of A- A Consciousness. CERA is a layered control architecture. CRANIUM is a runtime component for the management of specialized processors. g p p
  14. 14. Proposed Architecture (V) CERA-CRANIUM Perception Flow Agent’s  CERA World Body B d Physical Layer Sensor  Sensors S Accessible Mission‐specific Services environment Motor  Reachable  Core Layer Actuators environment Services
  15. 15. Proposed Architecture (VI) CRANIUM Workspace (at CERA Physical layer) CRANIUM  Single  Single Workspace Complex  Complex Percepts Percepts Percept  … Aggregators
  16. 16. Proposed Architecture (VII) CRANIUM Workspace Modulation Modulation Commands Core  Workspace Workspace Layer Single  Complex  Mission  Percepts Percepts Percepts … … Physical Layer Mission‐Specific Layer
  17. 17. Proposed Architecture (VIII) CRANIUM Workspace Modulation UT2004 Server CERA UT2004 Viewer UT2004 Viewer GameBots  2004 CERA‐CRANIUM CERA CRANIUM UT2004 Client UT2004 Client CCR / DSS  Translated  (Human player) Runtime Pogamut 2 UT2004 Client UT2004 Client .NET Framework (Spectator)
  18. 18. Proposed Architecture (IX) CERA-CRANIUM instantiation for UT2004 CERA sensory-motor services layer. CERA physical layer specialized processors. CERA mission-specific layer specialized p y p processors. CERA core layer goals and model state state.
  19. 19. Proposed Architecture (X) Example of percepts for UT2004 SP(Being Damaged) + SP(S E See Enemy) + CP(Enemy Approaching) = MP(EEnemy Att ki ) Attacking
  20. 20. Evaluation (I) Assessing believability is not straighforward. Subjective process process. Depends on opponents behavior. Game score is not significant. AVERAGE PLAYER SCORE Rule-Based System Bot 19.2 Q-Learning Bot 5.2 Q-Learning and Expert Systems Hybrid Bot 11.3 CERA-CRANIUM CERA CRANIUM Bot 18.3 18 3
  21. 21. Conclusions The application of consciousness-inspired cognitive architectures is promising promising. We W need to incorporate learning algorithms d i l i l ih and long term (episodic and semantic) memory t th model. to the d l Improve ToM (opponent/friend modeling).
  22. 22. Thank you 22