Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance, a wide range of intelligent escape methodologies has been developed in recent years. This paper presents a comprehensive survey of state-of-the-art research work on intelligent escape of robotic systems. Four main methods of intelligent escape are reviewed, including planning-based methodologies, partitioning-based methodologies, learning-based methodologies, and bio-inspired methodologies. The strengths and limitations of existing methods are summarized. In addition, potential applications of intelligent escape are discussed in various domains, such as search and rescue, evacuation, military security, and healthcare. In an effort to develop new approaches to intelligent escape, this survey identifies current research challenges and provides insights into future research trends in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System

The Language of the cybersouls

Debates about technology are mostly staged in a grand setting: presented as the sinister toolkit of authoritarian organisations, or as the progressive fabric of the future. Yet everyone is involved in technology as part of every microscopic action in every humdrum life. How do theories about technology cope with this political spectrum? One option is to treat technology as we treat language and specifically use Wittgenstein’s language games as an analogy. So that technology, like language, becomes inseparable from us and an intimate part of us, and this merging of technology and the human transforms us all into cyborgs

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Ms Pac-Man versus Ghost Team CEC 2011 competition

Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE

Statistical properties for directional alignment and chasing of players in football games

Focusing on motion of two interacting players in football games, two velocity vectors for the pair of one player and the nearest opponent player exhibit strong alignment. Especially, we find that there exists a characteristic interpersonal distance $r\simeq 500$ cm below which the circular variance for their alignment decreases rapidly. By introducing the order parameter $\phi(t)$ in order to measure degree of alignment of players' velocity vectors, we also find that the angle distribution between the nearest players' velocity vectors becomes wrapped Cauchy ($\phi \lesssim 0.7$) and the mixture of von Mises and wrapped Cauchy distributions ($\phi \gtrsim 0.7$), respectively. To understand these findings, we construct a simple model for the motion of the two interacting players with the following rules: chasing between the players and the reset of the chasing. We numerically show that our model successfully reproduce the results obtained from the actual data. Moreover, from the numerical study, we find that there is another characteristic distance $r\simeq 1000$ cm below which player's chasing starts.Comment: 16pages, 12 figures, 3 table