A curiosity model for artificial agents

Curiosity is an inherent characteristic of the animal instinct, which stimulates the need to obtain further knowledge and leads to the exploration of the surrounding environment. In this document we present a computational curiosity model, which aims at simulating that kind of behavior on artificial agents. This model is influenced by the two main curiosity theories defended by psychologists – Curiosity Drive Theory and Optimal Arousal Model. By merging both theories, as well as aspects from other sources, we concluded that curiosity can be defined in terms of the agent’s personality, its level of arousal, and the interest of the object of curiosity. The interest factor is defined in terms of the importance of the object of curiosity to the agent’s goals, its novelty, and surprise. To assess the performance of the model in practice, we designed a scenario consisting of virtual agents exploring a tile-based world, where objects may exist. The performance of the model in this scenario was evaluated in incremental steps, each one introducing a new component to the model. Furthermore, in addition to empirical evaluation, the model was also subjected to evaluation by human observers. The results obtained from both sources show that our model is able to simulate curiosity on virtual agents and that each of the identified factors has its role in the simulation.info:eu-repo/semantics/acceptedVersio

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28

Structures, inner values, hierarchies and stages: essentials for developmental robot architectures

In this paper we try to locate the essential components needed for a developmental robot architecture. We take the vocabulary and the main concepts from Piaget’s genetic epistemology and Vygotsky’s activity theory. After proposing an outline for a general developmental architecture, we describe the architectures that we have been developing in the recent years - Petitagé and Vygovorotsky. According to this outline, various contemporary works in autonomous agents can be classified, in an attempt to get a glimpse into the big picture and make the advances and open problems visible

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur