USING COEVOLUTION IN COMPLEX DOMAINS

Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes

From Bounded Rationality to Behavioral Economics

The paper provides an brief overview of the “state of the art” in the theory of rational decision making since the 1950’s, and focuses specially on the evolutionary justification of rationality. It is claimed that this justification, and more generally the economic methodology inherited from the Chicago school, becomes untenable once taking into account Kauffman’s Nk model, showing that if evolution it is based on trial-and-error search process, it leads generally to sub- optimal stable solutions: the ‘as if’ justification of perfect rationality proves therefore to be a fallacious metaphor. The normative interpretation of decision-making theory is therefore questioned, and the two challenging views against this approach , Simon’s bounded rationality and Allais’ criticism to expected utility theory are discussed. On this ground it is shown that the cognitive characteristics of choice processes are becoming more and more important for explanation of economic behavior and of deviations from rationality. In particular, according to Kahneman’s Nobel Lecture, it is suggested that the distinction between two types of cognitive processes – the effortful process of deliberate reasoning on the one hand, and the automatic process of unconscious intuition on the other – can provide a different map with which to explain a broad class of deviations from pure ‘olympian’ rationality. This view requires re-establishing and revising connections between psychology and economics: an on-going challenge against the normative approach to economic methodology.Bounded Rationality, Behavioral Economics, Evolution, As If

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Morality as natural history

What are moral values and where do they come from? David Hume argued that moral values were the product of a range of passions, inherent to human nature, that aim at the common good of society. Recent developments in game theory, evolutionary biology, animal behaviour, psychology and neuroscience suggest that Hume was right to suppose that humans have such passions. This dissertation reviews these developments, and considers their implications for moral philosophy. I first explain what Darwinian adaptations are, and how they generate behaviour. I then explain that, contrary to the Hobbesian caricature of life in the state of nature, evolutionary theory leads us to expect that organisms will be social, cooperative and even altruistic under certain circumstances. I introduce four main types of cooperation: kin altruism, coordination to mutual advantage, reciprocity and conflict resolution and provide examples of "adaptations for cooperation" from nonhuman species. I then review the evidence for equivalent adaptations for cooperation in humans. Next, I show how this Humean-Darwinian account of the moral sentiments can be used to make sense of traditional positions in meta-ethics; how it provides a rich deductive framework in which to locate and make sense of a wide variety of apparently contradictory positions in traditional normative ethics; and how it clearly demarcates the problems of applied ethics. I defend this version of ethical naturalism against the charge that it commits "the naturalistic fallacy". I conclude that evolutionary theory provides the best account yet of the origins and status of moral values, and that moral philosophy should be thought of as a branch of natural history

The evolution of coordinated cooperative behaviors

Cooperative behaviors are useful to many species of animals. Predators may team up to hunt large prey that they cannot catch on their own, while prey can herd together for defense. This dissertation focuses on understanding the environmental factors and cognitive architectures underlying the evolution of coordinated cooperation. An agent-based neuroevolutionary simulation of an ecosystem containing teams of predators and prey was built, modeling the environment of spotted hyenas. Communication, prey-capture rewards and reward-sharing strategies were found to determine whether cooperative hunting behaviors emerged. This simulation was extended to more complex cooperative behavior, that of coordinated mobbing. Through careful coordination, a large number of spotted hyenas can attack a group of lions and successfully steal a kill, even though lions are much stronger. The computational model developed in this dissertation helped understand how spotted hyenas are able to perform this complex cooperative task and how mobbing behaviors evolved. Many factors that were observed affecting lion-hyena interaction and rates of lion-mobbing in nature were also discovered using the model. This model was then used to make predictions about real-life hyena behaviors during mobbing events, which may be verified in the field in future. These results and predictions lead to general insights into how coordinated cooperative behaviors arise in humans and animals. Such insights in turn should prove useful in building cognitive architectures and team strategies for artificial agents in the future.Computer Science

A Temporal Framework for Hypergame Analysis of Cyber Physical Systems in Contested Environments

Game theory is used to model conflicts between one or more players over resources. It offers players a way to reason, allowing rationale for selecting strategies that avoid the worst outcome. Game theory lacks the ability to incorporate advantages one player may have over another player. A meta-game, known as a hypergame, occurs when one player does not know or fully understand all the strategies of a game. Hypergame theory builds upon the utility of game theory by allowing a player to outmaneuver an opponent, thus obtaining a more preferred outcome with higher utility. Recent work in hypergame theory has focused on normal form static games that lack the ability to encode several realistic strategies. One example of this is when a player’s available actions in the future is dependent on his selection in the past. This work presents a temporal framework for hypergame models. This framework is the first application of temporal logic to hypergames and provides a more flexible modeling for domain experts. With this new framework for hypergames, the concepts of trust, distrust, mistrust, and deception are formalized. While past literature references deception in hypergame research, this work is the first to formalize the definition for hypergames. As a demonstration of the new temporal framework for hypergames, it is applied to classical game theoretical examples, as well as a complex supervisory control and data acquisition (SCADA) network temporal hypergame. The SCADA network is an example includes actions that have a temporal dependency, where a choice in the first round affects what decisions can be made in the later round of the game. The demonstration results show that the framework is a realistic and flexible modeling method for a variety of applications

A lineage explanation of human normative guidance: the coadaptive model of instrumental rationality and shared intentionality.

This paper aims to contribute to the existing literature on normative cognition by providing a lineage explanation of human social norm psychology. This approach builds upon theories of goal-directed behavioral control in the reinforcement learning and control literature, arguing that this form of control defines an important class of intentional normative mental states that are instrumental in nature. I defend the view that great ape capacities for instrumental reasoning and our capacity (or family of capacities) for shared intentionality coadapted to each other and argue that the evolution of this capacity has allowed the representation of social norms and the emergence of our capacity for normative guidance

“Economic man” in cross-cultural perspective: Behavioral experiments in 15 small-scale societies

Researchers from across the social sciences have found consistent deviations from the predictions of the canonical model of self-interest in hundreds of experiments from around the world. This research, however, cannot determine whether the uniformity results from universal patterns of human behavior or from the limited cultural variation available among the university students used in virtually all prior experimental work. To address this, we undertook a cross-cultural study of behavior in ultimatum, public goods, and dictator games in a range of small-scale societies exhibiting a wide variety of economic and cultural conditions. We found, first, that the canonical model – based on self-interest – fails in all of the societies studied. Second, our data reveal substantially more behavioral variability across social groups than has been found in previous research. Third, group-level differences in economic organization and the structure of social interactions explain a substantial portion of the behavioral variation across societies: the higher the degree of market integration and the higher the payoffs to cooperation in everyday life, the greater the level of prosociality expressed in experimental games. Fourth, the available individual-level economic and demographic variables do not consistently explain game behavior, either within or across groups. Fifth, in many cases experimental play appears to reflect the common interactional patterns of everyday life