6,378 research outputs found
Affect and believability in game characters:a review of the use of affective computing in games
Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics
obtained by a population of intelligence agents, driven by million-agent
reinforcement learning. Our intention is to put intelligent agents into a
simulated natural context and verify if the principles developed in the real
world could also be used in understanding an artificially-created intelligent
population. To achieve this, we simulate a large-scale predator-prey world,
where the laws of the world are designed by only the findings or logical
equivalence that have been discovered in nature. We endow the agents with the
intelligence based on deep reinforcement learning (DRL). In order to scale the
population size up to millions agents, a large-scale DRL training platform with
redesigned experience buffer is proposed. Our results show that the population
dynamics of AI agents, driven only by each agent's individual self-interest,
reveals an ordered pattern that is similar to the Lotka-Volterra model studied
in population biology. We further discover the emergent behaviors of collective
adaptations in studying how the agents' grouping behaviors will change with the
environmental resources. Both of the two findings could be explained by the
self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International
Conference on Autonomous Agents and Multiagent Systems
OperA/ALIVE/OperettA
Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft
Negotiating Socially Optimal Allocations of Resources
A multiagent system may be thought of as an artificial society of autonomous
software agents and we can apply concepts borrowed from welfare economics and
social choice theory to assess the social welfare of such an agent society. In
this paper, we study an abstract negotiation framework where agents can agree
on multilateral deals to exchange bundles of indivisible resources. We then
analyse how these deals affect social welfare for different instances of the
basic framework and different interpretations of the concept of social welfare
itself. In particular, we show how certain classes of deals are both sufficient
and necessary to guarantee that a socially optimal allocation of resources will
be reached eventually
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