38,716 research outputs found
Cultural Learning in a Dynamic Environment: an Analysis of Both Fitness and Diversity in Populations of Neural Network Agents
Evolutionary learning is a learning model that can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher/pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. This paper examines the effects of cultural learning on the evolutionary process of a population of neural networks. In particular, the paper examines the genotypic and phenotypic diversity of a population as well as its fitness. Using these measurements, it is possible to examine the effects of cultural learning on the population's genetic makeup. Furthermore, the paper examines whether cultural learning provides a more robust learning mechanism in the face of environmental changes. Three benchmark tasks have been chosen as the evolutionary task for the population: the bit-parity problem, the game of tic-tac-toe and the game of connect-four. Experiments are conducted with populations employing evolutionary learning alone and populations combining evolutionary and cultural learning in an environment that changes dramatically.Cultural Learning, Dynamic Environments, Diversity, Multi-Agent Systems, Artificial Life
Modeling the Evolution of Artifact Capabilities in Multi-Agent Based Simulations
Cognitive scientists agree that the exploitation of objects as tools or artifacts has played a significant role in the evolution of human societies. In the realm of autonomous agents and multi-agent systems, a recent artifact theory proposes the artifact concept as an abstraction for representing functional system components that proactive agents may exploit towards realizing their goals. As a complement, the cognition of rational agents has been extended to accommodate the notion of artifact capabilities denoting the reasoning and planning capacities of agents with respect to artifacts. Multi-Agent Based Simulation (MABS) a well established discipline for modeling complex social systems, has been identified as an area that should benefit from these theories. In MABS the evolution of artifact exploitation can play an important role in the overall performance of the system. The primary contribution of this dissertation is a computational model for integrating artifacts into MABS. The emphasis of the model is on an evolutionary approach that facilitates understanding the effects of artifacts and their exploitation in artificial social systems over time. The artifact theories are extended to support agents designed to evolve artifact exploitation through a variety of learning and adaptation strategies. The model accents strategies that benefit from the social dimensions of MABS. Realized with evolutionary computation methods specifically genetic algorithms, cultural algorithms and multi-population cultural algorithms, artifact capability evolution is supported at individual, population and multi-population levels. A generic MABS and case studies are provided to demonstrate the use of the model in new and existing MABS systems. The accommodation of artifact capability evolution in artificial social systems is applicable in many domains, particularly when the modeled system is one where artifact exploitation is relevant to the evolution of the society and its overall behavior. With artifacts acknowledged as major contributors to societal evolution the impact of our model is significant, providing advanced tools that enable social scientists to analyze their findings. The model can inform archaeologists, economists, evolution theorists, sociologists and anthropologists among others
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Human-Agent Decision-making: Combining Theory and Practice
Extensive work has been conducted both in game theory and logic to model
strategic interaction. An important question is whether we can use these
theories to design agents for interacting with people? On the one hand, they
provide a formal design specification for agent strategies. On the other hand,
people do not necessarily adhere to playing in accordance with these
strategies, and their behavior is affected by a multitude of social and
psychological factors. In this paper we will consider the question of whether
strategies implied by theories of strategic behavior can be used by automated
agents that interact proficiently with people. We will focus on automated
agents that we built that need to interact with people in two negotiation
settings: bargaining and deliberation. For bargaining we will study game-theory
based equilibrium agents and for argumentation we will discuss logic-based
argumentation theory. We will also consider security games and persuasion games
and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729
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