Artificial Intelligence

It brings out the animal in us " is often heard, when speaking of unaltruistic behavior. Frans de Waal has argued against a " veneer theory " of one of humanity's most valued traits: morality. It has been proposed that morality emerges as a result of a system of evolutionary processes, giving rise to social altruistic instincts. Traditional research has been arguing that fully-fledged cognitive systems were required to give each individual its autonomy. In this paper, we propose that a simple sense of morality can evolve from swarms of agents picking actions such that they are viable to the survival of the whole group. In order to illustrate the emergence of a moral sense within a community of individuals, we use an asynchronous evolutionary model, simulating populations of simulated agents performing a foraging task on a two-dimensional map. We discuss the morality of each emergent behavior within each population, then subsequently analyze several cases of interactions between different evolved foraging strategies, which we argue bring some insight on the concept of morality out of a group, or across species.This proposed approach brings a new perspective on the way morality can be studied in an artificial model, in terms of adaptive behavior, corroborating the argument in which morality can be defined not only in highly cognitive species, but across all levels of complexity in life.

In India the quality of the wheat storage warehouse is assessed manually by officials and there is no scientific model present for the same. In this paper we have developed a model for the quality assessment using the Analytical Hierarchy Process and the Back Propagation Neural Network. The simulations are carried out in MATLAB software and the results are deduced thereafter. The results and the correlation between actual results and the deduced results show the validity of the developed model. It provides an effective way to assess the quality in short time and with a prescribed scientific model.

Nick Bostrom’s recently patched “simulation argument” (Bostrom, 2003, Bostrom and Kulczycki, 2011) purports to demonstrate the probability that we “live” now in an “ancestor simulation” – that is as a simulation of a period prior to that in which a civilization more advanced than our own – “post-human” - becomes able to simulate such a state of affairs as ours. As such simulations resemble "brains in vats" (BIVs) and may appear open to similar objections, the paper begins by reviewing objections to BIV type proposals, specifically those due a presumed mad envatter. In counter example, we explore the motivating rationale behind current work in the development of psychologically realistic social simulations. Further concerns about rendering human cognition in a computational medium are confronted through review of current dynamic systems models of cognitive agency. In these models, aspects of the human condition are reproduced that may in other forms be considered incomputable, i.e. political voice, predictive planning and consciousness. The paper then argues that simulations afford a unique potential to secure a post-human future, and may be necessary for a pre-post-human civilization like our own to achieve and to maintain a post-human situation. Long-standing philosophical interest in tools of this nature for Aristotle’s “statesman” and more recently for E.O. Wilson in the 1990s is observed. Self-extinction level threats from State and individual levels of organization are compared, and a likely dependence on large-scale psychologically realistic simulations to get past self-extinction level threats is projected. In the end, Bostrom's basic argument for the conviction that we exist now in a simulation is reaffirmed.

Much thought has been given to the question of who bears moral and legal responsibility for actions performed by robots. Some argue that responsibility could be attributed to a robot if it possessed human-like autonomy and metavolitionality, and that while such capacities can potentially be possessed by a robot with a single spatially compact body, they cannot be possessed by a spatially disjunct, decentralized collective such as a robotic swarm or network. However, advances in ubiquitous robotics and distributed computing open the door to a new form of robotic entity that possesses a unitary intelligence, despite the fact that its cognitive processes are not confined within a single spatially compact, persistent, identifiable body. Such a “nonlocalizable” robot may possess a body whose myriad components interact with one another at a distance and which is continuously transforming as components join and leave the body. Here we develop an ontology for classifying such robots on the basis of their autonomy, volitionality, and localizability. Using this ontology, we explore the extent to which nonlocalizable robots—including those possessing cognitive abilities that match or exceed those of human beings—can be considered moral and legal actors that are responsible for their own actions.

Design of a robust controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system can be a challenging work. This research paper focuses on the design and analysis of a high performance PID like fuzzy controller for flexible robot manipulator, in presence of uncertainties. In this research, fuzzy logic controller is a stable nonlinear controller, which selected to control of our nonlinear system. The proposed approach effectively combines of design methods from linear Proportional-Integral-Derivative (PID) controller and fuzzy logic theory to improve the performance, stability and robustness of the flexible robot manipulator. To solve system’s dynamic nonlinearity, the PID fuzzy logic controller is used as a PID like fuzzy logic controller. The PID like fuzzy logic controller is updated based on gain updating factor. In this methodology, fuzzy logic controller is used to estimate the dynamic uncertainties. In this methodology, PID like fuzzy logic controller is evaluated. PID like fuzzy logic controller has three inputs, Proportional (P), Derivative (D), and Integrator (I), if each inputs have 𝑁 linguistic variables to defined the dynamic behavior, it has 𝑁×𝑁×𝑁 linguistic variables. To solve this challenge, parallel structure of a PD-like fuzzy controller and PI-like fuzzy controller is evaluated. In the next step, the challenge of design PI and PD fuzzy rule tables are supposed to be solved. To solve this challenge PID like fuzzy controller is replaced by PD-like fuzzy controller with the integral term in output. This method is caused to design only PD type rule table for PD like fuzzy controller and PI like fuzzy controller.

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This article presents a computer simulated artificial intelligence (AI) agent that is able to move and interact in 2D and 3D environments. The agent has two operating modes: Manual Mode and Map or Autopilot mode. In the Manual mode the user has full control over the agent and can move it in all possible directions depending on the environment. In addition to that, the designed agent avoids hitting any obstacle by sensing them from a certain distance. The second and most important mode is the Map mode, in which the user can create a custom map, assign a starting and target location, and add predefined and sudden obstacles. The agent will then move to the target location by finding the shortest path avoiding any collision with any obstacle during the agent’s journey.

The article suggests as a solution, an algorithm that can help the agent to find the shortest path to a predefined target location in a complex 3D environment, such as cities and mountains, avoiding all predefined and sudden obstacles. It also avoids these obstacles during manual control and moves the agent to a safe location automatically.

The complexity of behavioural interactions in predator-prey systems has recently begun to capture trait-effects, or non-lethal effects, of predators on prey via induced behavioural changes. Non-lethal predation effects play crucial
roles in shaping population and community dynamics, particularly by inducing changes to foraging, movement, and
reproductive behaviours of prey. Prey exhibit tradeoffs in behaviours while minimizing predation risk. We use a
novel evolutionary ecosystem simulation EcoSim to study such behavioural interactions and their effects on prey
populations, thereby addressing the need for integrating multiple layers of complexity in behavioural ecology. EcoSim
allows complex intra- and inter-specific interactions between individual evolving behavioural models called prey, as
well as complex predator-prey dynamics and coevolution in a tri-trophic and spatially heterogeneous world. We
investigated the effects of predation risk on prey energy allocation and fitness. Results revealed that prey energy
budgets, life history traits, allocation of energy to movements and fitness-related actions differed greatly between prey
subjected to low-predation risk versus high-predation risk. High-predation risk suppressed prey foraging activity,
increased movement, and decreased reproduction relative to low-risk. Our study clearly shows that predation risk
alone induces behavioural changes in prey which drastically affect population and community dynamics, and when
interpreted within the evolutionary context of our simulation indicate that genetic changes accompanying coevolution
have long-term effects of prey adaptability to the absence of predators.