Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework

Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird

ORPHEUS: raisonnement et prédiction avec des représentations hétérogènes en utilisant la simulation

Interactive virtual environments pose a wide variety of challenges for intelligent agents, especially to make decisions in order to reach their goals. The difficulty of decision making tasks rises quickly when introducing continuous space and real time. It also becomes increasingly harder to build intelligent agents that can meaningfully interpret and act in unknown situations. In this thesis, we take inspiration from cognitive science, specifically from how humans perform mental simulation to anticipate events in the world around them, with the aim of obtaining an autonomous agent that makes decisions and adapts itself to novel situations. The mental simulation paradigm enjoys significant interest from the cognitive science community, but computational approaches to mental simulation rely on specialised simulators for a given task and thus are limited to specific scenarios. Our contribution is a generic agent architecture (ORPHEUS) which supports decision-making based on the simulation of functional models of the world ahead of time, inspired from how humans imagine the outer world and the outcomes of their actions based on the state of the real environment. The novelty of our approach consists in its ability to integrate both physical and behavioural predictions into the same framework, based on heterogeneous mental models which are used to evolve internal, imaginary scenarios within the agent. We apply our generic architecture to different contexts, including artificial intelligence competitions, which require the agent using our approach to perform physical and behavioural anticipation in continuous space and time. We evaluate the applicability of our approach to realistic conditions such as noisy perception, decision time constraints and imperfect world models. Results demonstrate the genericness of our approach from one scenario to another without modifying the agent architecture, and highlight the possible uses of the proposed mental simulation framework.Les environnements virtuels interactifs présentent une grande variété de défis pour les agents intelligents, en particulier le fait de prendre des décisions afin d'atteindre leurs objectifs. La difficulté des tâches de prise de décision augmente rapidement en introduisant la continuité d'espace et le temps réel. Il devient également de plus en plus difficile de construire des agents intelligents qui peuvent agir dans des situations inconnues et interpréter ces situations de manière cohérente. Dans cette thèse, nous nous inspirons des sciences cognitives, en particulier de la façon dont les humains exécutent la simulation mentale pour anticiper les événements dans le monde autour d'eux, dans le but d'obtenir un agent autonome qui prend des décisions et s'adapte aux nouvelles situations. Le paradigme de la simulation mentale bénéficie d'un intérêt significatif de la communauté des sciences cognitives, mais les approches computationnelles sur la simulation mentale reposent sur des simulateurs spécialisés pour une tâche donnée et sont donc limitées à des scénarios spécifiques. Notre contribution est une architecture générique d'agents (ORPHEUS) qui soutient la prise de décision basée sur la simulation, en amont, de modèles fonctionnels du monde. Cette approche est inspirée de la façon dont les humains imaginent le monde extérieur et les résultats de leurs actions basées sur l'état de l'environnement réel. La nouveauté de notre approche consiste en sa capacité à intégrer les prévisions à la fois physiques et comportementales dans le même cadre applicatif, sur la base de modèles mentaux hétérogènes qui sont utilisés pour faire évoluer des scénarios imaginaires internes au sein de l'agent. Nous appliquons notre architecture générique à différents contextes, y compris les compétitions sur l'intelligence artificielle, qui nécessitent que l'agent utilisant notre approche effectue l'anticipation physique et comportementale dans l'espace et le temps continu. Nous évaluons l'applicabilité de notre approche à des conditions réalistes comme la perception bruitée, les décisions en temps limité et les modèles imparfaits du monde. Les résultats démontrent le caractère générique de notre approche d'un scénario à l'autre, sans modification de l'architecture de l'agent, et mettent en évidence les utilisations possibles du cadre proposé de simulation mentale

MirrorBot: Using Human-inspired Mirroring Behavior To Pass A Turing Test

Abstract—Believability of automated characters in virtual worlds has posed a challenge for many years. In this paper, the author discusses a novel approach of using human-inspired mirroring behavior in MirrorBot, an Unreal Tournament 2004 game bot which crossed the humanness barrier and won the 2K BotPrize 2012 competition with the score of 52.2%, a record in the five year history of this contest. A comparison with past contest entries is presented and the relevance of the mirroring behavior as a humanness improvement factor is argued. The modules that compose MirrorBot’s architecture are presented along with a discussion of the advantages of this approach and proposed solutions for its drawbacks. The contribution continues with a discussion of the bot’s results in humanness and judging accuracy. I

Soybean Plant Disease Identification Using Convolutional Neural Network

International audiencePlants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. However, plant diseases are threatening the livelihood of this important source. Convolutional neural networks (CNN) have demonstrated great performance (beating that of humans) in object recognition and image classification problems. This paper describes the feasibility of CNN for plant disease classification for leaf images taken under the natural environment. The model is designed based on the LeNet architecture to perform the soybean plant disease classification. 12,673 samples containing leaf images of four classes, including the healthy leaf images, were obtained from the PlantVillage database. The images were taken under uncontrolled environment. The implemented model achieves 99.32% classification accuracy which show clearly that CNN can extract important features and classify plant diseases from images taken in the natural environment

ORPHEUS: Mental Simulation as Supportfor Decision-Making in a Virtual Agent

International audienc

Simulation within simulation for agent decision-making: Theoretical foundations from cognitive science to operational computer model

International audienceThis article deals with artificial intelligence models inspired from cognitive science. The scope of this paper is the simulation of the decision-making process for virtual entities. The theoretical framework consists of concepts from the use of internal behavioral simulation for human decision-making. Inspired from such cognitive concepts, the contribution consists in a computational framework that enables a virtual entity to possess an autonomous world of simulation within the simulation. It can simulate itself (using its own model of behavior) and simulate its environment (using its representation of other entities). The entity has the ability to anticipate using internal simulations, in complex environments where it would be extremely difficult to use formal proof methods. Comparing the prediction and the original simulation, its predictive models are improved through a learning process. Illustrations of this model are provided through two implementations. First illustration is an example showing a shepherd, his herd and dogs. The dog simulates the sheep’s behavior in order to make predictions testing different strategies. Second, an artificial 3D juggler plays in interaction with virtual jugglers, humans and robots. For this application, the juggler predicts the behavior of balls in the air and uses prediction to coordinate its behavior in order to juggle

A system for panoramic navigation inside a 3D environment

This paper presents a physical user interface intended to help the user (or multiple simultaneous users) to achieve an intuitive movement inside a 3D environment without using common interaction devices such as mouse or keyboard, while stressing the aspect of reducing financial investments. After exposing an analysis of current solutions and implementations of related topics, we argument our implementation and give detailed aspects of hardware and software architecture of the system, as well as a comprehensive efficiency study and explore the use cases with people with motor impairment. As future work, we intend to extend the usability of the system and release it under the GNU General Public License (GPL) for free use and further development by other parties

Temporal Deep Belief Network for Online Human Motion Recognition

International audienc