The lure of understanding biological intelligence has long occupied researchers. Success has always been measured in peer review, number of citations, or how influential some piece of work is in inspiring the next generation of re- searchers. What human-robot interaction (HRI) and artificial intelligence (AI) promises is a metric of believability that is not intrinsic to the values of the researcher or community of practice but to the utility and successful function of the robotic artifact within a larger society. This paper is a reflec- tion and response to the hypothesis that HRI is a pure, funda- mental art of artificial intelligence and the last great successor to a domain fraught with the trappings of an art that lost its way.

We present a multi-layer hierarchical connectionist network model for simulating human autobiographical memory in believable agents. Grounded in psychological theory, this model improves on previous attempts to model agents’ event knowledge by providing a more dynamic and non-deterministic representation of autobiographical memories. From this model, a Java-based proof-of-concept prototype system was created for use as an enabling technology in video games. This prototype was leveraged in the creation of a Minecraft modification (mod) implementation of the model that is able to demonstrate context-dependent recall and the effects of recency on memory recall. Wider implications of the model in agent and game design are discussed.

ABSTRACT In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, "giving the feeling of being controlled by a player", and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed. INTRODUCTION Nowadays, more and more consoles and video games are designed to make the player feel like he/she is in the game. To define how well this goal is achieved, two criteria have been defined in academic research: immersion and presence. According to Slater, immersion is an objective criterion which depends on the hardware and software(Slater et al. 1995). It includes criteria based on virtual sensory information's types, variety, richness, direction and in which extend they override real ones. For example, force feedback and motion sensing controllers, surround sound and high dynamic range rendering can improve the immersion. Presence, also known as telepresence (Steuer 1992), is a more subjective criterion.