Initiatives to electrify urban transport promote the purchase and usage of Electric Vehicles (EVs) and have great potential to mitigate the pressing challenges of climate change, energy scarcity and local air quality. Transportation electrification is a huge innovation and could directly and indirectly impact and/or be impacted by several urban sub-systems. This project develops an agent-based integrated framework for investigating how the EV market expands in the context of urban evolution at the micro scale, and assessing the potential impacts of the market expansion on the environment, power grid system and transport facilities, considering the interactions and dynamics found there. The framework may be useful for stakeholders, such as governments, as an aid to decision making. The integrated framework, SelfSim-EV, is updated from a Land Use and Transport (L-T) model, SelfSim, by incorporating several EV-related modules, including an EV market model, an activity-based travel demand model, a transport facility development model and a social network model. In order to somewhat present the behavioural rules of some key agents in SelfSim-EV, two questionnaire surveys on individual EV travel and purchase behaviours were delivered to members of the general public in Beijing, and semi-structured interviews with EV stakeholders were also carried out. The collected data was analysed using discrete choice models and Geographic Information System (GIS). SelfSim-EV was fully tested within two test cases in China, Baoding (a medium-sized city) and Beijing (the capital of China): first, parameter Sensitivity Analyses (SAs) were carried out to test SelfSim-EV within the test case of Baoding from both global and local perspectives, investigating the relationships between the 127 model parameters and 78 outputs of interest; Then SelfSim-EV was further tested within the case study of Beijing, involving in model initialisation, calibration, validation and prediction. Specifically, the SA results were used to calibrate SelfSim-EV in Beijing from 2011 to 2014 by matching various observed and simulated data types at both city- and district-levels, and the calibrated SelfSim-EV model was further validated against historical data in 2015. Then the future of EVs in Beijing was explored within a Reference Scenario (RefSc) from 2016 to 2020. Due to the model uncertainty in future events, several "what-if" scenarios were set up with the SelfSim-EV Beijing model to explore how three typical types of driving factors, namely policy, technology and infrastructure, may influence the EV market expansion at both aggregate and disaggregate levels. The results indicate that policies tend to be more influential than technologies and infrastructures in terms of EV penetration rates. RefSc eventually shows some improvement in total emissions, however, boosting sales of EVs (particularly PHEVs) in the wrong way could have negative impacts. Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system in RefSc, and it does not increase linearly as the EV sales rise. Slow charging posts appear to be necessary, whereas fast charging facilities seem to contribute slightly to the EV market expansion and thus may be not necessary at the current stage.

Social-ecological systems (SES) may become “locked in” particular states or configurations due to various constraints on adaptability imposed by feedback mechanisms or by processes designed to incentivize certain behavior. While these locked-in states may be desirable and robust to disturbances over relatively short time periods, limits on system adaptations may diminish the longer-term resilience of these states, and potentially of the system itself. The agricultural SES in the Iowa-Cedar River Basin in eastern Iowa is one such system. While highly productive, culturally important, and essential to local economies, the system is facing significant economic and environmental challenges. This dissertation presents the results of a project designed to survey the adaptability of farmers in the ICRB, model their actions subject to constraints, and plot potential future states under scenarios of climate change, policy, and market conditions. We utilize a coupled agent-based model (ABM) to examine the specified resilience of the system to future climate, leveraging the ability of ABMs to integrate heterogeneous actors, dynamic couplings of natural and human systems, and processes across spatiotemporal scales. We find that farmer behavior is primarily constrained by economic factors, including federal crop insurance subsidies and the financial risk of implementing different crops or practices. Finally, we generate alternative system trajectories by modeling twenty-one scenarios, identifying actionable adaptations and pathways for transforming the system to alternative, more sustainable states.

We study the computational complexity of collecting items inside a grid map with obstacles, using an agent that always slides to the maximal extend, until it is stopped by an obstacle. An agent could be, for example, a robot or a vehicle, while obstacles could be walls or other immovable objects, and items could be packages that need to be picked up. This problem has very natural applications in robotics. The restricted type of motion of the agent naturally models movement on a frictionless surface, and movement of a robot with limited sensing capabilities and thus limited localization. For example, if a robot cannot determine the distance traveled once it starts moving, then it makes sense to keep moving until an obstacle is reached, even if the robot has a map of the environment. With today’s technology it is possible to create sophisticated robots but, since the complexity and the costs of such robots are high, it is sometimes better to use simple inexpensive robots that can still solve relatively complex tasks. In fact, simple robots are quite common and usually built using simple sensors that have limited capabilities, but that are easy to use and are considerably cheaper than more sophisticated ones. The computational complexity of numerous problems with movable objects has been extensively studied before. However, only a few of them have maximal sliding agents, and they usually do not have the goal of collecting items. We show that the problem of deciding if all the items can be collected by a maximal sliding agent can be solved efficiently when the agent is the only moving object in the map. However, we show that optimization problems such as determining the minimum number of moves required to collect all the items, and also variants in more complex environments are computationally intractable. Hence, for those problems it is better to focus on using heuristics than on finding optimal solutions.

This thesis examines theoretical and popular ways of knowing the prostitute and the client. Its purpose is to intervene in contemporary ways of knowing and articulate a more consistent feminist stance on prostitution. Currently, the prostitute is known predominantly through the discourse of psychology whilst the client is known through the discourse of sexology. She is deviant and he is normal. She is a victim and he is an agent. The issue of inconsistency in the feminist stance on prostitution is related to the recognition that these dualisms figure in the way in which all knowledge of the client and the prostitute is organised. Within feminist theory the prostitute is known through the dualism of victim and agent whilst the client is known through the sex/gender distinction. The former perpetuates certain ways of knowing the prostitute that cannot embrace the complexity and ambivalence of prostitution for women. If she is a victim she is only passive and exploited. If she is an agent she is both active and free. Utilising the latter allows the client to escape scrutiny. This thesis will argue that this is for two reasons. Firstly, because feminists have tended to support the idea of the prostitute as agent within the victim/agent dichotomy. Within such a way of knowing, any critique of the client became a critique of the livelihood of the prostitute, and is best avoided. Secondly, because feminists tend to work within the sex/gender distinction and its associated dualisms of mind and body, nature and culture. As such, they tend to perpetuate, rather than challenge, the sexological relationship between the sexual and the social. In both analyses, the sexual urge is ultimately natural, albeit modified by society. Analyses that argue for the social constitution of sexuality (rather than simply its social construction) still perpetuate the sex/gender distinction by claiming the validity of the mind/body dualism for their analyse. This thesis will argue that these dualisms structure an impossible choice for feminists and help to position them within the divisive prostitution debate. In a political climate that perpetuates only two ways of knowing prostitution, to critique prostitution is to be anti-sex, moralising, prudish and conservative. In contrast, to support prostitution is hailed as pro-sex, pro-women and pro-choice. Within this dichotomising of the political issue, feminists gain either conservative or libertarian allies. Within such a political climate, a consistent feminist position is lost. In order to counter this political and theoretical inconsistency, this thesis argues for a connection between the dualisms through the organisation of modern liberal democracies. To know the prostitute through the victim/agent dichotomy and the client through the sex/gender distinction (and associated dualisms of mind and body, nature and culture) is also to call upon the public/private split as their organising feature. The public/private split gives meaning to the dualisms of victim and agent, sex and gender, mind and body, through its role in the perpetuation of associations between victim, body, sex, private and women, and between agent, gender, mind, public and men. This thesis will argue that these dualisms are not useful for explaining the ambivalent and contradictory status of prostitution as both work and sex, public and private, rational and irrational, embodied and disembodied, sexual and social. However, not only does prostitution challenge the explanatory value of these dualisms, but the experience of prostitution for the prostitute and the client both subverts and inverts these dualisms. The usual configuration of the dualisms public/private, worker/consumer, male/female, mind/body, rationality/irrationality, are public, worker, male, mind, rationality, in contrast to private, consumer, female, body, irrationality. The prostitute is positioned in and through modern liberal democracies as embodied, but claims the status of worker through her experience of disembodiment. The client is positioned in and through modern liberal democracies as disembodied, and continues this proprietorial relationship with his body during the prostitution contract. She becomes the embodied worker and he becomes the disembodied sex partner. This further demonstrates the inability of a dualistic conception of prostitution to take into account the ambivalent and contradictory status of the prostitute and the client. Whilst this thesis will suggest that such an ambivalent status is to be found in all relations between men and women in modern liberal democracies, it will also propose the political implications of this theoretical reconfiguration for the feminist position on prostitution.

This project details the development of a novel and practical framework for the development of conversation agents (CAs), or conversation robots. CAs, are software programs which can be used to provide a natural interface between human and computers. In this study, ‘conversation’ refers to real-time dialogue exchange between human and machine which may range from web chatting to “on-the-go” conversation through mobile devices. In essence, the project proposes a “smart and effective” communication technology where an autonomous agent is able to carry out simulated human conversation via multiple channels. The CA developed in this project is termed “Artificial Intelligence Natural-language Identity” (AINI) and AINI is used to illustrate the implementation and testing carried out in this project. Up to now, most CAs have been developed with a short term objective to serve as tools to convince users that they are talking with real humans as in the case of the Turing Test. The traditional designs have mainly relied on ad-hoc approach and hand-crafted domain knowledge. Such approaches make it difficult for a fully integrated system to be developed and modified for other domain applications and tasks. The proposed framework in this thesis addresses such limitations. Overcoming the weaknesses of previous systems have been the key challenges in this study. The research in this study has provided a better understanding of the system requirements and the development of a systematic approach for the construction of intelligent CAs based on agent architecture using a modular N-tiered approach. This study demonstrates an effective implementation and exploration of the new paradigm of Computer Mediated Conversation (CMC) through CAs. The most significant aspect of the proposed framework is its ability to re-use and encapsulate expertise such as domain knowledge, natural language query and human-computer interface through plug-in components. As a result, the developer does not need to change the framework implementation for different applications. This proposed system provides interoperability among heterogeneous systems and it has the flexibility to be adapted for other languages, interface designs and domain applications. A modular design of knowledge representation facilitates the creation of the CA knowledge bases. This enables easier integration of open-domain and domain-specific knowledge with the ability to provide answers for broader queries. In order to build the knowledge base for the CAs, this study has also proposed a mechanism to gather information from commonsense collaborative knowledge and online web documents. The proposed Automated Knowledge Extraction Agent (AKEA) has been used for the extraction of unstructured knowledge from the Web. On the other hand, it is also realised that it is important to establish the trustworthiness of the sources of information. This thesis introduces a Web Knowledge Trust Model (WKTM) to establish the trustworthiness of the sources. In order to assess the proposed framework, relevant tools and application modules have been developed and an evaluation of their effectiveness has been carried out to validate the performance and accuracy of the system. Both laboratory and public experiments with online users in real-time have been carried out. The results have shown that the proposed system is effective. In addition, it has been demonstrated that the CA could be implemented on the Web, mobile services and Instant Messaging (IM). In the real-time human-machine conversation experiment, it was shown that AINI is able to carry out conversations with human users by providing spontaneous interaction in an unconstrained setting. The study observed that AINI and humans share common properties in linguistic features and paralinguistic cues. These human-computer interactions have been analysed and contributed to the understanding of how the users interact with CAs. Such knowledge is also useful for the development of conversation systems utilising the commonalities found in these interactions. While AINI is found having difficulties in responding to some forms of paralinguistic cues, this could lead to research directions for further work to improve the CA performance in the future.

Doctor of Philosophy / Computational models of motivation are software reasoning processes designed to direct, activate or organise the behaviour of artificial agents. Models of motivation inspired by psychological motivation theories permit the design of agents with a key reasoning characteristic of natural systems: experience-based attention focus. The ability to focus attention is critical for agent behaviour in complex or dynamic environments where only small amounts of available information is relevant at a particular time. Furthermore, experience-based attention focus enables adaptive behaviour that focuses on different tasks at different times in response to an agent’s experiences in its environment. This thesis is concerned with the synthesis of motivation and reinforcement learning in artificial agents. This extends reinforcement learning to adaptive, multi-task learning in complex, dynamic environments. Reinforcement learning algorithms are computational approaches to learning characterised by the use of reward or punishment to direct learning. The focus of much existing reinforcement learning research has been on the design of the learning component. In contrast, the focus of this thesis is on the design of computational models of motivation as approaches to the reinforcement component that generates reward or punishment. The primary aim of this thesis is to develop computational models of motivation that extend reinforcement learning with three key aspects of attention focus: rhythmic behavioural cycles, adaptive behaviour and multi-task learning in complex, dynamic environments. This is achieved by representing such environments using context-free grammars, modelling maintenance tasks as observations of these environments and modelling achievement tasks as events in these environments. Motivation is modelled by processes for task selection, the computation of experience-based reward signals for different tasks and arbitration between reward signals to produce a motivation signal. Two specific models of motivation based on the experience-oriented psychological concepts of interest and competence are designed within this framework. The first models motivation as a function of environmental experiences while the second models motivation as an introspective process. This thesis synthesises motivation and reinforcement learning as motivated reinforcement learning agents. Three models of motivated reinforcement learning are presented to explore the combination of motivation with three existing reinforcement learning components. The first model combines motivation with flat reinforcement learning for highly adaptive learning of behaviours for performing multiple tasks. The second model facilitates the recall of learned behaviours by combining motivation with multi-option reinforcement learning. In the third model, motivation is combined with an hierarchical reinforcement learning component to allow both the recall of learned behaviours and the reuse of these behaviours as abstract actions for future learning. Because motivated reinforcement learning agents have capabilities beyond those of existing reinforcement learning approaches, new techniques are required to measure their performance. The secondary aim of this thesis is to develop metrics for measuring the performance of different computational models of motivation with respect to the adaptive, multi-task learning they motivate. This is achieved by analysing the behaviour of motivated reinforcement learning agents incorporating different motivation functions with different learning components. Two new metrics are introduced that evaluate the behaviour learned by motivated reinforcement learning agents in terms of the variety of tasks learned and the complexity of those tasks. Persistent, multi-player computer game worlds are used as the primary example of complex, dynamic environments in this thesis. Motivated reinforcement learning agents are applied to control the non-player characters in games. Simulated game environments are used for evaluating and comparing motivated reinforcement learning agents using different motivation and learning components. The performance and scalability of these agents are analysed in a series of empirical studies in dynamic environments and environments of progressively increasing complexity. Game environments simulating two types of complexity increase are studied: environments with increasing numbers of potential learning tasks and environments with learning tasks that require behavioural cycles comprising more actions. A number of key conclusions can be drawn from the empirical studies, concerning both different computational models of motivation and their combination with different reinforcement learning components. Experimental results confirm that rhythmic behavioural cycles, adaptive behaviour and multi-task learning can be achieved using computational models of motivation as an experience-based reward signal for reinforcement learning. In dynamic environments, motivated reinforcement learning agents incorporating introspective competence motivation adapt more rapidly to change than agents motivated by interest alone. Agents incorporating competence motivation also scale to environments of greater complexity than agents motivated by interest alone. Motivated reinforcement learning agents combining motivation with flat reinforcement learning are the most adaptive in dynamic environments and exhibit scalable behavioural variety and complexity as the number of potential learning tasks is increased. However, when tasks require behavioural cycles comprising more actions, motivated reinforcement learning agents using a multi-option learning component exhibit greater scalability. Motivated multi-option reinforcement learning also provides a more scalable approach to recall than motivated hierarchical reinforcement learning. In summary, this thesis makes contributions in two key areas. Computational models of motivation and motivated reinforcement learning extend reinforcement learning to adaptive, multi-task learning in complex, dynamic environments. Motivated reinforcement learning agents allow the design of non-player characters for computer games that can progressively adapt their behaviour in response to changes in their environment.

Research Doctorate - Doctor of Philosophy (PhD) / Mobile agents are programs that travel autonomously through a computer network in order to perform some computation or gather information on behalf of a human user or an application. In the last several years numerous applications of mobile agents have emerged, including e-commerce. However, mobile agent paradigm introduces a number of security threats both to the agents themselves and to the servers that they visit. This thesis gives an overview of the main security issues related to the mobile agent paradigm. The first part of the thesis focuses on security of mobile agent itself. In this part, we propose a new coupling technique based on trust as a social control to work together with existing traditional security mechanisms. It relies on the “reputation” of the hosts in the itinerary and ensures that the agent succeeds in accomplishing its task with a high probability. Due to the fact that the coupling technique requires an agent’s itinerary to be known in advance, we introduce two new concepts: a “Scout mobile agent”, whose primary purpose is to determine the itinerary required for accomplishing a given task, and a “Routed mobile agent”, which operates with an itinerary known in advance. This enables the Routed agent to incorporate various security mechanisms, including our new coupling technique. Our Routed agent technique is also applicable independently of the Scout agent, whenever the itinerary and the trust values of the platforms in the itinerary are known. We also proposed a Petrol Station as an entity that would provide a service to other entities, in the form of certifying mobile agents and equipping them with safe itinerary based on trust score and applying the Routed agent. In the second part of the thesis, we shed some light on the security of mobile agent platforms as it is considered more critical than the security of agents. In particular, we consider a scenario where a platform hosts a database containing confidential individual information and allows mobile agentstoquery the data base. This mobile agent maybe behave maliciously which is similar to an intruder in the Statistical Disclosure Control(SDC), where measuring disclosure risk is still considered as a difficult and only partly solved problem[111]. We introduce a scenario that is not adequately covered by any of the previous discloser risk measures. Shannon’s entropy can be considered a satisfactory measure for the disclosure risk that is related to the exact compromise. However, in the case of approximate compromise, we argue that Shannon’s entropy does not express precisely the intruder’s knowledge about a particular confidential value. We introduce a novel disclosure risk measure that is based on Shannon’s entropy but covers both exact and approximate compromise. The main advantage of our measure over previously proposed measures that it gives careful consideration to the attribute values in addition to the probabilities with which the values occur. We use a dynamic programming algorithm to calculate the disclosure risk for various levels of approximate compromise. Importantly, our proposed measure is independent of the applied SDC technique. Finally, we show how this measure can be used to evaluate the security mechanisms for protecting privacy in statistical databases and data mining. We conduct extensive experiments and apply our proposed security measure to three different data sets protected by three different SDC techniques, namely Sampling, Query Restriction, and Noise Addition.

<p>Studien utgår från en artikel i Svenskt näringsliv som handlar om regelförenkling för företagen. Artikeln anger att 30 procent av företagen ser myndighetskraven, i form av svåra regler, som ett hinder för deras företag att växa. För att minska de administrativa kostnaderna för företagen har regeringen satt upp ett mål att kostnaderna ska minskas med 25 procent fram till år 2010. Regeringen har därför gett myndigheterna i uppdrag att ta fram handlingsplaner för regelförenkling, detta för att förenkla rapporteringen för företagen.</p><p>I studien har undersökts hur hanteringen hos myndigheterna ser ut, då det gäller rapportering av lagstadgade uppgifter som företagen är skyldiga att rapportera. Hur ser användningen av rapporterna ut och är all rapportering nödvändig. Vi valde att genomföra undersökningen genom att studera ett utvalt företag och de lagstadgade rapporter som företaget skickar in till myndigheter. Öppna intervjuer har genomförts med företaget och de berörda myndigheterna.</p><p>Teorierna om transparens, intressentmodellen/resursberoendeteorin och agentteorin är utgångspunkt för genomförandet av studien. Transparens för att undersöka om företagen har förståelse för användningen av de uppgifter som rapporteras in till myndigheterna. För att undersöka om det finns behov av rapporteringen från företagets intressenter, används intressentmodellen. Agentteorin valdes för att undersöka hur samarbetet mellan myndigheterna och företagen fungerar</p><p>Resultatet av undersökningen visar att rapporteringen som företagen gör till myndigheterna används framförallt till att kontrollera att företagen följer den för verksamheten gällande lagstiftningen. Rapporterna används även till att göra branschspecifika sammanställningar som intressenter kan ta del av. Undersökningen visar att det finns brister i myndigheternas kontroll. Då redovisningen är allt för omfattande för att myndigheterna ska kontrollera alla uppgifter i rapporterna. Det visade sig i undersökningen att företaget inte hade några problem att förstå tillvägagångssättet vid rapporteringen eller användningen av den.</p>

The design of the control laws governing the behavior of individual agents is crucial for the successful development of cooperative agent teams. These control laws may utilize a combination of local and/or global knowledge to achieve the resulting group behavior. A key difficulty in this development is deciding the proper balance between local and global control required to achieve the desired emergent group behavior. This paper addresses this issue by presenting some general guidelines and principles for determining the appropriate level of global versus local control. These principles are illustrated and implemented in a "keep formation'' cooperative task case study.

This thesis has two parts, each with a different subject. Part 1 studies the macroeconomic implications of alternative health care reforms. Part 2 studies the computation and simulation of dynamic competitive equilibria in models with heterogeneous agents and market frictions. In 2007, 44.5 million non-elderly in the U.S did not have health insurance coverage. Empirical studies suggest that there are serious negative consequences associated with uninsurance. Consequently, there is wide agreement that reforming the current health care system is desirable and several proposals have been discussed among economists and in the political arena. However, little attention has been paid to quantify the macroeconomic consequences of reforming the health insurance system in the U.S. The objective of this section is to develop a theoretical framework to evaluate a broad set of health care reform plans. I build a model that is capable of reproducing a set of key facts of health expenditure and insurance demand patterns, as well as key macroeconomic conditions of the U.S. during the last decade. Then, I use this model to derive the macroeconomic implications of alternative reforms and alternative ways of funding these reforms. The second part of this thesis studies the computation and simulation of dynamic competitive equilibria in models with heterogeneous agents and market frictions. This type of models have been of considerable interest in macroeconomics and finance to analyze the effects of various macroeconomic policies, the evolution of wealth and income distribution, and the variability of asset prices. However, there is no reliable algorithm available to compute their equilibria. We develop a theoretical framework for the computation and simulation of dynamic competitive markets economies with heterogeneous agents and market frictions. We apply these methods to some macroeconomic models and find important improvements over traditional methods.