Research in human-level AI using computer games

2001

understand what is required for human-level artificial intelligence. A key component of our methodology is developing AI systems that behave in complex, dynamic environments with many of the properties of the world we inhabit. Although robotics might seem an obvious choice, research in robotics requires solving many difficult problems related to low-level sensing and acting in the real world far removed from the cognitive aspects of intelligence. Simulated virtual environments make it possible to bypass many of these problems, while preserving the need for intelligent real-time decision-making and interaction. Unfortunately, development of realistic virtual environments is an expensive and time-consuming enterprise onto itself and requires expertise in many areas far afield from artificial intelligence. However, computer games provide us with a source of cheap, reliable, and flexible technology for developing our own virtual environments for research. Over the last four years, we ha...

Robotics and Vision …

We have modified the public-domain Quake II game to support research and teaching. Our research is in multi-agent control and supporting human-computer interfaces. Teaching applications have so far been in an undergraduate Artificial Intelligence class and include natural language understanding, machine learning, computer vision, and production system control. The motivation for this report is mainly to document our system development and interface. Only early results are in, but they appear promising. Our source code and user-level documentation is available on the web. The information document is a somewhat motion-blurred snapshot of the situation in September 2004.

Computer, 2001

Building agents that can survive the harsh environment of a popular computer game provides fresh insight into the study of artificial intelligence.

Proceedings of the First International Workshop on Agents for Games and Simulations, 2009

Abstract. Many research projects oriented on control mechanisms of virtual agents in videogames have emerged in last years. However, this boost has not been accompanied with the emergence of toolkits supporting development of these projects, which slows down the progress of the field. Here, we present Pogamut 3, an open source platform for rapid development of behaviour of virtual agents embodied in a 3D environment of the Unreal Tournament 2004 videogame. Pogamut 3 is tailored to support research as well as ...

Computer games have belatedly come to the fore as a serious platform for AI research. Through our own experiments in the fields of imitation learning and intelligent agents, it became clear that the lack of a unified, powerful yet intuitive API was a serious impediment to the adoption of commercial games in both research and education. Parallel to our own specialised work, we therefore decided to develop a generalpurpose library for the creation of game agents, in the hope that the availability of such software would help stimulate further interest in the field. Though geared towards machinelearning, the API would be flexible enough to facilitate multiple forms of artificial intelligence, making it suitable for application in research and in undergraduate courses centring upon traditional AI and agent-based systems.

International Journal of Computer Applications

Computer games are an increasingly popular application for Artificial Intelligence(AI) research. This paper discusses some of the most interesting components and challenges faced by developers in designing and creation of a game based on artificial intelligence. Game AI provides players a richer gaming experience by going beyond scripted interactions, responsive interaction systems that are adaptive and intelligent.

1998

CMU), started work on a project to develop synthetic pilots for large-scale distributed simulations. The goal was to develop real-time high-fidelity models of human behavior. We built our synthetic pilots using an AI architecture called Soar ; . In October 1997, our entities participated in the Synthetic Theater of War, 97 (STOW-97). STOW-97 (Ceranowicz, 1998) was a DOD Advanced Concept Technology Demonstration (ACTD) that was integrated with United Endeavor 98-1, a USACOM sponsored exercise. As an ACTD, the overall goal of STOW-97 was to permit an early and inexpensive evaluation of advanced technologies that show promise for improving military effectiveness. STOW-97 provided a rigorous test of our entities because it involved large numbers of active entities (~4,000) in a realistic battlefield with synthetic environment (weather, day, night, dynamic terrain). In this paper, we describe the work done by the University of Michigan group, where we developed pilots for fixed wing aircraft, culminating in a system called TacAir-Soar. The group at USC/ISI developed pilots for helicopters , while the group at CMU did research on real-time natural language processing . This paper describes our approach to developing synthetic pilots for STOW-97, and its applicability to developing AI systems for computer games. Earlier papers discussed the lessons learned from developing the behaviors for STOW-97 (Laird et al., 1998a), and from integrating our software within the context of the larger simulation arena (Laird et al, 1998b). The point of our research is to develop a general, integrated architecture for building AI systems. Over the last fifteen years, Soar has been used for research in AI, including problem solving, planning and learning. Soar has also been used as the basis for detailed modeling of human behavior. Our agents for STOW are the largest and most complex systems ever developed in Soar. They, together with the helicopters entities developed by USC/ISI, are probably the first AI systems used in a large-scale military exercise. The application of this technology to computer games appears obvious. Current first-person perspective competitive games, such as Doom, Quake, or Descent, have relatively simple AI opponents and depend on numbers and fire power to make the game interesting. We wish to explore games where the AI opponent is on par with humans, so that each level is more like a deathmatch against one or two opponents, than a slugfest against many. We also envision high-fidelity AI systems that can substitute for real players in long term role playing games, where continual human play is not possible.

Springer eBooks, 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

National Conference on Artificial Intelligence, 2000

Although one of the fundamental goals of AI is to understand and develop intelligent systems that have all of the capabilities of humans, there is little active research directly pursuing that goal. We propose that AI for interactive computer games is an emerging application area in which this goal of human-level AI can successfully be pursued. Interactive computer games have

This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the Turing test for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of humanlike, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges' opinions.