In this paper we extend the cultural framework previously developed for the Village multi-agent s... more In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship, over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced reciprocal exchange. Agents, or households, are able to procure several resources. We use Cultural Algorithms as a framework for the emergence of social intelligence at both individual and cultural levels. Successful agents on both networks can promote themselves to be included in the hub network where they can develop exchange links to other hubs. The collective effect of the hub network is representative of the quality of life in the population and serves as indicator for motives behind the mysterious emigration from the region. Knowledge represents the development and use of exchange relationships between agents. The presence of defectors on the hub network improved resilience of the social system while maintaining the population size as that observed where no defectors were present. This suggests a tendency for the social system to favor larger hub formations over less social individuals or those with weaker ties.
We model a framework whereby agents decide how to allocate their time among available tasks. Agen... more We model a framework whereby agents decide how to allocate their time among available tasks. Agents learn from their previous experiences, adjusting the weights given to each task as a result. These social agents are also influenced by the experiences of others in their social networks, including kin and trading partners. Agents are allowed to trade surplus goods and to request goods that they need using various trading methods. We demonstrate this model using the Agent-Based Model of the Village Ecodynamics Project, which simulates the life of Pueblo farmers of the central Mesa Verde region between A.D. 600 and 1300.
The interactions between humans and their environment, comprising living and non-living entities,... more The interactions between humans and their environment, comprising living and non-living entities, can be studied via Social Network Analysis (SNA). Node classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in a bid to solve node classification and community detection problems in SNA. Hence, crucial aspects such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration in the course of analyzing the social network. These factors determine the nature and dynamics of a given social network. In this paper, we have proposed a unique framework, Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network structures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting features of the social graph, via learning the context of each actor with respect to neighboring actors/nodes, with the goal of generating vectorspace embedding per actor. Successively, these relatively low-dimensional vector embeddings are fed as input features to a downstream classifier for classification tasks about the social graph/network. Herein RLVECN has been trained, tested, and evaluated on real-world social networks.
The multi-agent Village simulation was initially developed to examine the settlement and farming ... more The multi-agent Village simulation was initially developed to examine the settlement and farming practicer of prehisppnic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado [1,21. The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispnnic Puebloan people from the Four Corners region after 700 years of occupation. The results suggested that other factors besides precipitation were important. We then proceeded to add economic factors into the simulation, first allowing agents to engage in reciprocal exchanges between kin. This resulted in larger populations, more complex social networks, and more resilient systems. However, the exchange was done randomly and individuals did not remember the transactions. I n this paper we explicitly embed the reciprocal exchange process within a Culturnl Algorithm, where individual agents can remember individuals that they have cooperated with. Also, in the cultural space the group can learn generalizations about what kind of relative is likely to successfully respond to a request. Thew generalizations are used to drive changes in requestor behavior. The results of this approach produced an even larger and more complex system exhibiting greater dependence cm huh nodes that are sensitive to precipitation.
Currently, the world seeks to find appropriate mitigation techniques to control and prevent the s... more Currently, the world seeks to find appropriate mitigation techniques to control and prevent the spread of the new SARS-CoV-2. In our paper herein, we present a peculiar Multi-Task Learning framework that jointly predicts the effect of SARS-CoV-2 as well as Personal-Protective-Equipment consumption in Community Health Centres with respect to a given socially interacting populace. Predicting the effect of the virus (SARS-CoV-2), via studies and analyses, enables us to understand the nature of SARS-CoV-2 with reference to factors that promote its growth and spread. Therefore, these foster widespread awareness; and the populace can become more proactive and cautious so as to mitigate the spread of Corona Virus Disease 2019 (COVID-19). Furthermore, understanding and predicting the demand for Personal Protective Equipment promotes the efficiency and safety of healthcare workers in Community Health Centres. Owing to the novel nature and strains of SARS-CoV-2, relatively few literature and research exist in this regard. These existing literature have attempted to solve the problem statement(s) using either Agent-based Models, Machine Learning Models, or Mathematical Models. In view of this, our work herein adds to existing literature via modeling our problem statements as Multi-Task Learning problems. Results from our research indicate that government actions and human factors are the most significant determinants that influence the spread of SARS-CoV-2.
Social Network Analysis (SNA) has become a very interesting research topic with regard to Artific... more Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.
International Journal on Artificial Intelligence Tools, Dec 1, 2006
In this paper we extend the cultural framework previously developed for the Village multi-agent s... more In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship, over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced reciprocal exchange. Agents, or households, are able to procure several resources. We use Cultural Algorithms as a framework for the emergence of social intelligence at both individual and cultural levels. Successful agents in both networks can promote themselves to be included in the hub network where they can develop exchange links to other hubs. The collective effect of the hub network is representative of the quality of life in the population and serves as an indicator for motives behind the mysterious emigration from the region. Knowledge represents the development and use of exchange relationships between agents. The presence of defectors in the hub network improved resilience of the social system while maintaining the population size at that observed where no defectors were present.
Explanations for the collapse of complex social systems including social, political, and economic... more Explanations for the collapse of complex social systems including social, political, and economic factors have been suggested. Here we add cultural factors into an agent-based model developed by Kohler for the Mesa Verde Prehispanic Pueblo region. W e employ a framework for modeling Cultural Evolution, Cultural Algorithms developed by Reynolds 119791. O u r approach investigates the impact that the emergent properties of a complex system will have on its resiliency as well as on its potential for collapse. T h a t is, if the system's social structure is brittle, any factor that is able exploit this fragility can cause a collapse of the system. In particular, we will investigate the impact that environmental variability in the Mesa Verde had on the formation of social networks among agents. Specifically we look at how t h e spatial distribution of agricultural land and the temporal distribution of rainfall impacts the systems structure. We show that the distribution of agricultural resources is conducive to the generation of so called "small world" networks that require "conduits" or some agents of larger interconnectivity to link the small worlds together. Experiments show that there is a major decrease in these conduits in early 1200 A.D. This can have a serious potential impact on the networks resiliency. While the simulation shows an upturn near the start of the 14th century it is possible.that the damage to the network had already been done.
Agent interaction in a community, such as the online buyer-seller scenario, is often uncertain, a... more Agent interaction in a community, such as the online buyer-seller scenario, is often uncertain, as when an agent comes in contact with other agents they initially know nothing about each other. Currently, many reputation models are developed that help service consumers select better service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information or they are built upon witness information also known as word-of-mouth that involves the reports provided by others. Neither the interaction trust nor the witness information models alone succeed in such uncertain interactions. In this paper we propose a hybrid reputation model involving both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. A sample simulation is built to setup buyer-seller services and uncertain interactions. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputable service providers, eventually helping consumers obtain more gains. Furthermore, the trust model developed is used in calculating trust values of service providers.
Automotive manufacturers are under stressful timelines as they shift their focus from internal co... more Automotive manufacturers are under stressful timelines as they shift their focus from internal combustion engines (ICE) to electric (EV) and hybrid-electric vehicles (HEV). The demand for this rapid change is crucial to meet a growing consumer market. New manufacturing challenges coupled with rapid change can lead to substantial safety risks for consumers as well as financial liability for automakers, especially when recalls happen. The resulting misplacement, misalignment, or defective assembly of any of the components or connectors can result in critical or even fatal outcomes for consumers. Recent findings reported by CNBC revealed that the shift to electric vehicles had cost automakers billions of dollars (Kolodny 2022). The cost of recalling an EV far outweighs that of an ICE. For instance, the Ford Kuga plug-in HEV had re-calls costs of about $19,000 per vehicle, in contrast to a typical ICE vehicle recall that averages around $500 per vehicle (Isidore and Vales-Dapena 2022). ...
2018 Innovations in Intelligent Systems and Applications (INISTA)
A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new M... more A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new Multilevel Selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory which aims to improve the capability of MPCA to tackle evolution of cooperation. A two-level selection process is introduced namely within-group selection and between-group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. If the group reaches a certain size, it splits into two daughter groups. We test our algorithm on CEC 2015 expensive benchmark functions to evaluate its performance. We show that our proposed algorithm improves solution accuracy and consistency. The model can be extended to more than two levels of selection and can also include migration.
Adaptive Agents and Multi-Agents Systems, May 4, 2015
In this study a recent evolution and learning model for artifacts is extended to address the abil... more In this study a recent evolution and learning model for artifacts is extended to address the ability of artificial social agents to realize their goals by adapting the exploitation of dynamic artifacts in dynamic environments over time. 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. The dynamic landscape used for settling and farming is abstracted as an artifact and agents learn to adapt its exploitation over time by employing individual, social and population learning strategies. Comparing various strategies revealed learning through social networks while evolving the extent of the network as the best adaptive strategy. The results are consistent with archeological records as a wider margin is observed between social and non-social learners during periods known for the highest landscape variability. In addition, learning through social networks outperforms learning via cultural beliefs which is expected given the heterogeneity of the landscape.
Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory B... more Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network which implements a biologically plausible architecture, and a supervised learning mechanism. The proposed machine learning driven predictive model is benchmarked against a conventional logistic regression model, demonstrating statistically significant performance improvements.
Social networks have a dynamic nature so their structures change over time. In this paper, we pro... more Social networks have a dynamic nature so their structures change over time. In this paper, we propose a new evolutionary method to predict the state of a network in the near future by extracting knowledge from its current structure. This method is based on the fact that social networks consist of communities. Observing current state of a given network, the method calculates the probability of a relationship between each pair of individuals who are not directly connected to each other and estimate the chance of being connected in the next time slot. We have tested and compared the method on one synthetic and one large real dataset with 117i¾?185i¾?083 edges. Results show that our method can predict the next state of a network with a high rate of accuracy.
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016
I certify that I have obtained a written permission from the copyright owner(s) to include the ab... more I certify that I have obtained a written permission from the copyright owner(s) to include the above published material(s) in my dissertation. I certify that the above material describes work completed during my registration as graduate student at the University of Windsor. I declare that, to the best of my knowledge, my dissertation does not infringe upon anyone's copyright nor violate any proprietary rights and that any ideas, techniques, quotations, or any other material from the work of other people included in my dissertation, published or otherwise, are fully acknowledged in accordance with I would like to take the opportunity at the beginning of this dissertation document to acknowledge those who played an important positive role in my doctoral studies journey. First and foremost, I would like to thank my PhD advisor, Dr. Ziad Kobti for his continuous support, kind and professional guidance during almost 5 years of my doctoral studies. His remarks and advises always illuminated the pathway to fulll one of my major dreams to get to this level of expertise.
Emotions are an integral part of human decision making. It follows that emotions should take part... more Emotions are an integral part of human decision making. It follows that emotions should take part in the decision process towards the design of more realistic artificial agents. Three psychological models for emotions are examined and a corresponding algorithm is developed for each depicting its process. A generalized multi-agent model is designed to demonstrate the implementation of each of the three methods. An agent thus represents a human capable of exhibiting emotional state in response to an arbitrary emotionally charged event of varying impact.
In recent work we introduced an agent based model prototype to enable intervention studies by sim... more In recent work we introduced an agent based model prototype to enable intervention studies by simulating the effect of the use of vehicle restraint systems on injury levels in children passengers. In a socially motivated dynamic framework, modeled drivers, or agents, are able to identify kin and neighbor relations. A knowledge structure to capture the driver's knowledge of the perceived correct child seat selection and location configuration is implemented. In this study, using a cultural algorithm, with situational knowledge dominant in the belief space, we enable both positive and negative exemplars in order to guide the belief at the population level. Based on evolving individual experiences, and corresponding changes in the belief system, the presence of both positive and negative exemplars were shown to be influential on the overall children population health and improved driver correctness in selecting the correct child restraint.
The dynamic and complex nature of real world systems makes it difficult to build an accurate arti... more The dynamic and complex nature of real world systems makes it difficult to build an accurate artificial simulation. Agent Based Modeling Simulations used to build such simulated models are often oversimplified and not realistic enough to predict reliable results. In addition to this, the validation of such Agent Based Model (ABM) involves great difficulties thus putting a question mark on their effective usage and acceptability. One of the major problems affecting the reliability of ABM is the dynamic nature of the environment. An ABM initially validated at a given time stamp is bound to become invalid with the inevitable change in the environment over time. Thus, an ABM not learning regularly from its environment cannot sustain its validity over a longer period of time. This thesis describes a novel approach for incorporating adaptability and learning in an ABM simulation, in order to improve and maintain its prediction accuracy under dynamic environment. In addition, it also intends to identify and study the effect of various factors on the overall progress of the ABM, based on the proposed approach. v Dedication I would like to dedicate this thesis to my family, especially my mother, whose prayers and immense patience and faith in me have got me this far in my life. Also, I am grateful to the Almighty God for his blessings. vi I would like to thank my supervisor, Dr. Ziad Kobti, for giving me the opportunity to get an exposure of the research field. It has been a great experience working under his guidance, which got me a publication, which otherwise would have been impossible. His constant motivation, support and faith guided me in successful completion of my thesis. I would also like to appreciate him for all the financial support he has given, which helped me focus on my work and took care of my expenses. My sincere gratitude goes to Mrs. Gloria Mensah, secretary to the director, who has always helped setup meetings with my supervisor and ensured that he always meets me, despite his busy schedule. In addition, my sincere thanks to Ms. Mandy Turkalj, who has always been there to take care of other issues related to my master's degree. Finally, I would like to thank my parents, especially my mother who has gone through all the ups and downs; I faced during my research, along with me.
In this paper we extend the cultural framework previously developed for the Village multi-agent s... more In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship, over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced reciprocal exchange. Agents, or households, are able to procure several resources. We use Cultural Algorithms as a framework for the emergence of social intelligence at both individual and cultural levels. Successful agents on both networks can promote themselves to be included in the hub network where they can develop exchange links to other hubs. The collective effect of the hub network is representative of the quality of life in the population and serves as indicator for motives behind the mysterious emigration from the region. Knowledge represents the development and use of exchange relationships between agents. The presence of defectors on the hub network improved resilience of the social system while maintaining the population size as that observed where no defectors were present. This suggests a tendency for the social system to favor larger hub formations over less social individuals or those with weaker ties.
We model a framework whereby agents decide how to allocate their time among available tasks. Agen... more We model a framework whereby agents decide how to allocate their time among available tasks. Agents learn from their previous experiences, adjusting the weights given to each task as a result. These social agents are also influenced by the experiences of others in their social networks, including kin and trading partners. Agents are allowed to trade surplus goods and to request goods that they need using various trading methods. We demonstrate this model using the Agent-Based Model of the Village Ecodynamics Project, which simulates the life of Pueblo farmers of the central Mesa Verde region between A.D. 600 and 1300.
The interactions between humans and their environment, comprising living and non-living entities,... more The interactions between humans and their environment, comprising living and non-living entities, can be studied via Social Network Analysis (SNA). Node classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in a bid to solve node classification and community detection problems in SNA. Hence, crucial aspects such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration in the course of analyzing the social network. These factors determine the nature and dynamics of a given social network. In this paper, we have proposed a unique framework, Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network structures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting features of the social graph, via learning the context of each actor with respect to neighboring actors/nodes, with the goal of generating vectorspace embedding per actor. Successively, these relatively low-dimensional vector embeddings are fed as input features to a downstream classifier for classification tasks about the social graph/network. Herein RLVECN has been trained, tested, and evaluated on real-world social networks.
The multi-agent Village simulation was initially developed to examine the settlement and farming ... more The multi-agent Village simulation was initially developed to examine the settlement and farming practicer of prehisppnic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado [1,21. The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispnnic Puebloan people from the Four Corners region after 700 years of occupation. The results suggested that other factors besides precipitation were important. We then proceeded to add economic factors into the simulation, first allowing agents to engage in reciprocal exchanges between kin. This resulted in larger populations, more complex social networks, and more resilient systems. However, the exchange was done randomly and individuals did not remember the transactions. I n this paper we explicitly embed the reciprocal exchange process within a Culturnl Algorithm, where individual agents can remember individuals that they have cooperated with. Also, in the cultural space the group can learn generalizations about what kind of relative is likely to successfully respond to a request. Thew generalizations are used to drive changes in requestor behavior. The results of this approach produced an even larger and more complex system exhibiting greater dependence cm huh nodes that are sensitive to precipitation.
Currently, the world seeks to find appropriate mitigation techniques to control and prevent the s... more Currently, the world seeks to find appropriate mitigation techniques to control and prevent the spread of the new SARS-CoV-2. In our paper herein, we present a peculiar Multi-Task Learning framework that jointly predicts the effect of SARS-CoV-2 as well as Personal-Protective-Equipment consumption in Community Health Centres with respect to a given socially interacting populace. Predicting the effect of the virus (SARS-CoV-2), via studies and analyses, enables us to understand the nature of SARS-CoV-2 with reference to factors that promote its growth and spread. Therefore, these foster widespread awareness; and the populace can become more proactive and cautious so as to mitigate the spread of Corona Virus Disease 2019 (COVID-19). Furthermore, understanding and predicting the demand for Personal Protective Equipment promotes the efficiency and safety of healthcare workers in Community Health Centres. Owing to the novel nature and strains of SARS-CoV-2, relatively few literature and research exist in this regard. These existing literature have attempted to solve the problem statement(s) using either Agent-based Models, Machine Learning Models, or Mathematical Models. In view of this, our work herein adds to existing literature via modeling our problem statements as Multi-Task Learning problems. Results from our research indicate that government actions and human factors are the most significant determinants that influence the spread of SARS-CoV-2.
Social Network Analysis (SNA) has become a very interesting research topic with regard to Artific... more Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.
International Journal on Artificial Intelligence Tools, Dec 1, 2006
In this paper we extend the cultural framework previously developed for the Village multi-agent s... more In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship, over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced reciprocal exchange. Agents, or households, are able to procure several resources. We use Cultural Algorithms as a framework for the emergence of social intelligence at both individual and cultural levels. Successful agents in both networks can promote themselves to be included in the hub network where they can develop exchange links to other hubs. The collective effect of the hub network is representative of the quality of life in the population and serves as an indicator for motives behind the mysterious emigration from the region. Knowledge represents the development and use of exchange relationships between agents. The presence of defectors in the hub network improved resilience of the social system while maintaining the population size at that observed where no defectors were present.
Explanations for the collapse of complex social systems including social, political, and economic... more Explanations for the collapse of complex social systems including social, political, and economic factors have been suggested. Here we add cultural factors into an agent-based model developed by Kohler for the Mesa Verde Prehispanic Pueblo region. W e employ a framework for modeling Cultural Evolution, Cultural Algorithms developed by Reynolds 119791. O u r approach investigates the impact that the emergent properties of a complex system will have on its resiliency as well as on its potential for collapse. T h a t is, if the system's social structure is brittle, any factor that is able exploit this fragility can cause a collapse of the system. In particular, we will investigate the impact that environmental variability in the Mesa Verde had on the formation of social networks among agents. Specifically we look at how t h e spatial distribution of agricultural land and the temporal distribution of rainfall impacts the systems structure. We show that the distribution of agricultural resources is conducive to the generation of so called "small world" networks that require "conduits" or some agents of larger interconnectivity to link the small worlds together. Experiments show that there is a major decrease in these conduits in early 1200 A.D. This can have a serious potential impact on the networks resiliency. While the simulation shows an upturn near the start of the 14th century it is possible.that the damage to the network had already been done.
Agent interaction in a community, such as the online buyer-seller scenario, is often uncertain, a... more Agent interaction in a community, such as the online buyer-seller scenario, is often uncertain, as when an agent comes in contact with other agents they initially know nothing about each other. Currently, many reputation models are developed that help service consumers select better service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information or they are built upon witness information also known as word-of-mouth that involves the reports provided by others. Neither the interaction trust nor the witness information models alone succeed in such uncertain interactions. In this paper we propose a hybrid reputation model involving both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. A sample simulation is built to setup buyer-seller services and uncertain interactions. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputable service providers, eventually helping consumers obtain more gains. Furthermore, the trust model developed is used in calculating trust values of service providers.
Automotive manufacturers are under stressful timelines as they shift their focus from internal co... more Automotive manufacturers are under stressful timelines as they shift their focus from internal combustion engines (ICE) to electric (EV) and hybrid-electric vehicles (HEV). The demand for this rapid change is crucial to meet a growing consumer market. New manufacturing challenges coupled with rapid change can lead to substantial safety risks for consumers as well as financial liability for automakers, especially when recalls happen. The resulting misplacement, misalignment, or defective assembly of any of the components or connectors can result in critical or even fatal outcomes for consumers. Recent findings reported by CNBC revealed that the shift to electric vehicles had cost automakers billions of dollars (Kolodny 2022). The cost of recalling an EV far outweighs that of an ICE. For instance, the Ford Kuga plug-in HEV had re-calls costs of about $19,000 per vehicle, in contrast to a typical ICE vehicle recall that averages around $500 per vehicle (Isidore and Vales-Dapena 2022). ...
2018 Innovations in Intelligent Systems and Applications (INISTA)
A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new M... more A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new Multilevel Selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory which aims to improve the capability of MPCA to tackle evolution of cooperation. A two-level selection process is introduced namely within-group selection and between-group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. If the group reaches a certain size, it splits into two daughter groups. We test our algorithm on CEC 2015 expensive benchmark functions to evaluate its performance. We show that our proposed algorithm improves solution accuracy and consistency. The model can be extended to more than two levels of selection and can also include migration.
Adaptive Agents and Multi-Agents Systems, May 4, 2015
In this study a recent evolution and learning model for artifacts is extended to address the abil... more In this study a recent evolution and learning model for artifacts is extended to address the ability of artificial social agents to realize their goals by adapting the exploitation of dynamic artifacts in dynamic environments over time. 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. The dynamic landscape used for settling and farming is abstracted as an artifact and agents learn to adapt its exploitation over time by employing individual, social and population learning strategies. Comparing various strategies revealed learning through social networks while evolving the extent of the network as the best adaptive strategy. The results are consistent with archeological records as a wider margin is observed between social and non-social learners during periods known for the highest landscape variability. In addition, learning through social networks outperforms learning via cultural beliefs which is expected given the heterogeneity of the landscape.
Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory B... more Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network which implements a biologically plausible architecture, and a supervised learning mechanism. The proposed machine learning driven predictive model is benchmarked against a conventional logistic regression model, demonstrating statistically significant performance improvements.
Social networks have a dynamic nature so their structures change over time. In this paper, we pro... more Social networks have a dynamic nature so their structures change over time. In this paper, we propose a new evolutionary method to predict the state of a network in the near future by extracting knowledge from its current structure. This method is based on the fact that social networks consist of communities. Observing current state of a given network, the method calculates the probability of a relationship between each pair of individuals who are not directly connected to each other and estimate the chance of being connected in the next time slot. We have tested and compared the method on one synthetic and one large real dataset with 117i¾?185i¾?083 edges. Results show that our method can predict the next state of a network with a high rate of accuracy.
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016
I certify that I have obtained a written permission from the copyright owner(s) to include the ab... more I certify that I have obtained a written permission from the copyright owner(s) to include the above published material(s) in my dissertation. I certify that the above material describes work completed during my registration as graduate student at the University of Windsor. I declare that, to the best of my knowledge, my dissertation does not infringe upon anyone's copyright nor violate any proprietary rights and that any ideas, techniques, quotations, or any other material from the work of other people included in my dissertation, published or otherwise, are fully acknowledged in accordance with I would like to take the opportunity at the beginning of this dissertation document to acknowledge those who played an important positive role in my doctoral studies journey. First and foremost, I would like to thank my PhD advisor, Dr. Ziad Kobti for his continuous support, kind and professional guidance during almost 5 years of my doctoral studies. His remarks and advises always illuminated the pathway to fulll one of my major dreams to get to this level of expertise.
Emotions are an integral part of human decision making. It follows that emotions should take part... more Emotions are an integral part of human decision making. It follows that emotions should take part in the decision process towards the design of more realistic artificial agents. Three psychological models for emotions are examined and a corresponding algorithm is developed for each depicting its process. A generalized multi-agent model is designed to demonstrate the implementation of each of the three methods. An agent thus represents a human capable of exhibiting emotional state in response to an arbitrary emotionally charged event of varying impact.
In recent work we introduced an agent based model prototype to enable intervention studies by sim... more In recent work we introduced an agent based model prototype to enable intervention studies by simulating the effect of the use of vehicle restraint systems on injury levels in children passengers. In a socially motivated dynamic framework, modeled drivers, or agents, are able to identify kin and neighbor relations. A knowledge structure to capture the driver's knowledge of the perceived correct child seat selection and location configuration is implemented. In this study, using a cultural algorithm, with situational knowledge dominant in the belief space, we enable both positive and negative exemplars in order to guide the belief at the population level. Based on evolving individual experiences, and corresponding changes in the belief system, the presence of both positive and negative exemplars were shown to be influential on the overall children population health and improved driver correctness in selecting the correct child restraint.
The dynamic and complex nature of real world systems makes it difficult to build an accurate arti... more The dynamic and complex nature of real world systems makes it difficult to build an accurate artificial simulation. Agent Based Modeling Simulations used to build such simulated models are often oversimplified and not realistic enough to predict reliable results. In addition to this, the validation of such Agent Based Model (ABM) involves great difficulties thus putting a question mark on their effective usage and acceptability. One of the major problems affecting the reliability of ABM is the dynamic nature of the environment. An ABM initially validated at a given time stamp is bound to become invalid with the inevitable change in the environment over time. Thus, an ABM not learning regularly from its environment cannot sustain its validity over a longer period of time. This thesis describes a novel approach for incorporating adaptability and learning in an ABM simulation, in order to improve and maintain its prediction accuracy under dynamic environment. In addition, it also intends to identify and study the effect of various factors on the overall progress of the ABM, based on the proposed approach. v Dedication I would like to dedicate this thesis to my family, especially my mother, whose prayers and immense patience and faith in me have got me this far in my life. Also, I am grateful to the Almighty God for his blessings. vi I would like to thank my supervisor, Dr. Ziad Kobti, for giving me the opportunity to get an exposure of the research field. It has been a great experience working under his guidance, which got me a publication, which otherwise would have been impossible. His constant motivation, support and faith guided me in successful completion of my thesis. I would also like to appreciate him for all the financial support he has given, which helped me focus on my work and took care of my expenses. My sincere gratitude goes to Mrs. Gloria Mensah, secretary to the director, who has always helped setup meetings with my supervisor and ensured that he always meets me, despite his busy schedule. In addition, my sincere thanks to Ms. Mandy Turkalj, who has always been there to take care of other issues related to my master's degree. Finally, I would like to thank my parents, especially my mother who has gone through all the ups and downs; I faced during my research, along with me.
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