Learning and adaptation strategies for evolving artifact capabilities

In this study we address enhancing the ability of social agents embedded in multi-agent based simulations to achieve their goals by using objects in their environment as artifacts. Reformulated as a discrete optimization problem solved with evolutionary computation methods, social agents are empowered to learn and adapt through observations of their own behavior, others in the environment and their community at large. An implemented case study is provided incorporating the model into the multi-agent simulation of the Village EcoDynamics Project developed to study the early Pueblo Indian settlers from A.D. 600 to 1300. Eliminating the existing presumption that agents automatically know the productivity of the landscape as part of their settling and farming practices, agents use the landscape as an artifact, learning to predict its productivity from a few attributes such as the area's slope and aspect. Given the dynamic nature of the landscape and its inhabitants, agents also evolve various combinations of learning strategies adapting to meet their needs. The result is the demonstration of a mechanism for incorporating artifact use learning and evolution in social simulations, leading to the emergence of favorable learning strategies

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

Capso: A Multi-Objective Cultural Algorithm System To Predict Locations Of Ancient Sites

ABSTRACT CAPSO: A MULTI-OBJECTIVE CULTURAL ALGORITHM SYSTEM TO PREDICT LOCATIONS OF ANCIENT SITES by SAMUEL DUSTIN STANLEY August 2019 Advisor: Dr. Robert Reynolds Major: Computer Science Degree: Doctor of Philosophy The recent archaeological discovery by Dr. John O’Shea at University of Michigan of prehistoric caribou remains and Paleo-Indian structures underneath the Great Lakes has opened up an opportunity for Computer Scientists to develop dynamic systems modelling these ancient caribou routes and hunter-gatherer settlement systems as well as the prehistoric environments that they existed in. The Wayne State University Cultural Algorithm team has been interested assisting Dr. O’Shea’s archaeological team by predicting new structures in the Alpena-Amberley Ridge Region. To further this end, we developed a rule-based expert prediction system to work with our team’s dynamic model of the Paleolithic environment. In order to evolve the rules and thresholds within this expert system, we developed a Pareto-based multi-objective optimizer called CAPSO, which stands for Cultural Algorithm Particle Swarm Optimizer. CAPSO is fully parallelized and is able to work with modern multicore CPU architecture, which enables CAPSO to handle “big data” problems such as this one. The crux of our methodology is to set up a biobjective problem with the objectives being locations predicted by the expert system (minimize) vs. training set occupational structures within those predicted locations (maximize). The first of these quantities plays the role of “cost” while the second plays the role of “benefit”. Four separate such biobjective problems are created, one for each of the four relevant occupational structure types (hunting blinds, drive lines, caches, and logistical camps). For each of these problems, when CAPSO tunes the system’s rules and thresholds, it changes which locations are predicted and hence also which structures are flagged. By repeatedly tuning the rules and thresholds, CAPSO creates a Pareto Front of locations predicted vs. structures predicted for each of the four occupational structure types. Statistical analysis of these Pareto Fronts reveals that as the number of structures predicted (benefit) increases linearly, the number of locations predicted (cost) increases exponentially. This pattern is referred to in the dissertation as the Accelerating Cost Hypothesis (ACH). The ACH statistically holds for all four structure types, and is the result of imperfect information

The Role of Prior Knowledge in Multi-Population Cultural Algorithms for Community Detection in Dynamic Social Networks

The relationship between a community and the knowledge that it encompasses is a fundamentally important aspect of any social network. Communities, with some level of similarity, implicitly tend to have some level of similarity in their knowledge as well. This work does the analysis on the role of prior knowledge in Multi-Population Cultural Algorithm (MPCA) for community detection in dynamic social networks. MPCA can be used to find the communities in a social network. The knowledge gained in this process is useful to analyze the communities in other social networks having some level of similarity. Our work assumes that knowledge is an integral part of any community of a social network and plays a very important role in its evolution. Different types of networks with levels of non-similarity are analyzed to see the role of prior knowledge while finding communities in them

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Modeling the Evolution of Agent Capabilities and Specialization

A social system is a patterned network of interrelationships that exist between individuals, institutions, and groups forming a coherent whole. Understanding the varying system outcomes for different decision-making processes selected under varying environment constraints in advance will aid in the realization the of best decision towards an effective outcome. One of the ways to increase system productivity is ‘Agent Specialization’. Also, the agents (individuals) who operate as generalists are most vulnerable to being replaced. Therefore, there is a need to focus on agent specialization to enhance the ability of an agent along with the evolution of an agent. Multi-Agent Based Simulation, a subfield of distributed AI, provides a technique to naturally describe a social system. To help improve decision-making intricacies of the agents to evolve and specialize, there is an increasing need to formulate an enhanced model of MABS. This thesis proposes a novel framework that exploits the benefits of social networks providing a decision support system for agent (individual) specialization by integrating the concept of ‘Positive Social Influence’ exerted by experts in the system. Consequently, the proposed framework assists the growth of agents by enabling the evolution of agent capabilities with the identification of suitable producer-agents using an evolutionary component (cultural algorithms). Enabling agent specialization and assisting the ability of the agents through capability evolution is anticipated to increase the productivity of the system. Evaluation of results shows the successful evolution of agent capabilities with the identification of suitable producer-agents in an optimized aspect (reduced operational cost and reduced distance cost) in comparison with exhaustive search, random search, and genetic algorithms and the improved degree of specialization of agents (increased dol values with a minimum of 3% increase to a maximum of 16.7% increase in comparison with standard genetic threshold model for varying agents and task number) in a given dynamic environment

Social Network Analysis using Cultural Algorithms and its Variants

Finding relationships between social entities and discovering the underlying structures of networks are fundamental tasks for analyzing social networks. In recent years, various methods have been suggested to study these networks efficiently, however, due to the dynamic and complex nature that these networks have, a lot of open problems still exist in the field. The aim of this research is to propose an integrated computational model to study the structure and behavior of the complex social network. The focus of this research work is on two major classic problems in the field which are called community detection and link prediction. Moreover, a problem of population adaptation through knowledge migration in real-life social systems has been identified to model and study through the proposed method. To the best of our knowledge, this is the first work in the field which is exploring this concept through this approach. In this research, a new adaptive knowledge-based evolutionary framework is defined to investigate the structure of social networks by adopting a multi-population cultural algorithm. The core of the model is designed based on a unique community-oriented approach to estimate the existence of a relationship between social entities in the network. In each evolutionary cycle, the normative knowledge is shaped through the extraction of the topological knowledge from the structure of the network. This source of knowledge is utilized for the various network analysis tasks such as estimating the quality of relation between social entities, related studies regarding the link prediction, population adaption, and knowledge formation. The main contributions of this work can be summarized in introducing a novel method to define, extract and represent different sources of knowledge from a snapshot of a given network to determine the range of the optimal solution, and building a probability matrix to show the quality of relations between pairs of actors in the system. Introducing a new similarity metric, utilizing the prior knowledge in dynamic social network analysis and study the co-evolution of societies in a case of individual migration are another major contributions of this work. According to the obtained results, utilizing the proposed approach in community detection problem can reduce the search space size by 80%. It also can improve the accuracy of the search process in high dense networks by up to 30% compared with the other well-known methods. Addressing the link prediction problem through the proposed approach also can reach the comparable results with other methods and predict the next state of the system with a notably high accuracy. In addition, the obtained results from the study of population adaption through knowledge migration indicate that population with prior knowledge about an environment can adapt themselves to the new environment faster than the ones who do not have this knowledge if the level of changes between the two environments is less than 25%. Therefore, utilizing this approach in dynamic social network analysis can reduce the search time and space significantly (up to above 90%), if the snapshots of the system are taken when the level of changes in the network structure is within 25%. In summary, the experimental results indicate that this knowledge-based approach is capable of exploring the evolution and structure of the network with the high level of accuracy while it improves the performance by reducing the search space and processing time

Self-adaptive fitness in evolutionary processes

Most optimization algorithms or methods in artificial intelligence can be regarded as evolutionary processes. They start from (basically) random guesses and produce increasingly better results with respect to a given target function, which is defined by the process's designer. The value of the achieved results is communicated to the evolutionary process via a fitness function that is usually somewhat correlated with the target function but does not need to be exactly the same. When the values of the fitness function change purely for reasons intrinsic to the evolutionary process, i.e., even though the externally motivated goals (as represented by the target function) remain constant, we call that phenomenon self-adaptive fitness. We trace the phenomenon of self-adaptive fitness back to emergent goals in artificial chemistry systems, for which we develop a new variant based on neural networks. We perform an in-depth analysis of diversity-aware evolutionary algorithms as a prime example of how to effectively integrate self-adaptive fitness into evolutionary processes. We sketch the concept of productive fitness as a new tool to reason about the intrinsic goals of evolution. We introduce the pattern of scenario co-evolution, which we apply to a reinforcement learning agent competing against an evolutionary algorithm to improve performance and generate hard test cases and which we also consider as a more general pattern for software engineering based on a solid formal framework. Multiple connections to related topics in natural computing, quantum computing and artificial intelligence are discovered and may shape future research in the combined fields.Die meisten Optimierungsalgorithmen und die meisten Verfahren in Bereich künstlicher Intelligenz können als evolutionäre Prozesse aufgefasst werden. Diese beginnen mit (prinzipiell) zufällig geratenen Lösungskandidaten und erzeugen dann immer weiter verbesserte Ergebnisse für gegebene Zielfunktion, die der Designer des gesamten Prozesses definiert hat. Der Wert der erreichten Ergebnisse wird dem evolutionären Prozess durch eine Fitnessfunktion mitgeteilt, die normalerweise in gewissem Rahmen mit der Zielfunktion korreliert ist, aber auch nicht notwendigerweise mit dieser identisch sein muss. Wenn die Werte der Fitnessfunktion sich allein aus für den evolutionären Prozess intrinsischen Gründen ändern, d.h. auch dann, wenn die extern motivierten Ziele (repräsentiert durch die Zielfunktion) konstant bleiben, nennen wir dieses Phänomen selbst-adaptive Fitness. Wir verfolgen das Phänomen der selbst-adaptiven Fitness zurück bis zu künstlichen Chemiesystemen (artificial chemistry systems), für die wir eine neue Variante auf Basis neuronaler Netze entwickeln. Wir führen eine tiefgreifende Analyse diversitätsbewusster evolutionärer Algorithmen durch, welche wir als Paradebeispiel für die effektive Integration von selbst-adaptiver Fitness in evolutionäre Prozesse betrachten. Wir skizzieren das Konzept der produktiven Fitness als ein neues Werkzeug zur Untersuchung von intrinsischen Zielen der Evolution. Wir führen das Muster der Szenarien-Ko-Evolution (scenario co-evolution) ein und wenden es auf einen Agenten an, der mittels verstärkendem Lernen (reinforcement learning) mit einem evolutionären Algorithmus darum wetteifert, seine Leistung zu erhöhen bzw. härtere Testszenarien zu finden. Wir erkennen dieses Muster auch in einem generelleren Kontext als formale Methode in der Softwareentwicklung. Wir entdecken mehrere Verbindungen der besprochenen Phänomene zu Forschungsgebieten wie natural computing, quantum computing oder künstlicher Intelligenz, welche die zukünftige Forschung in den kombinierten Forschungsgebieten prägen könnten

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare