It is claimed that action discourse provides us with a criterion of adequacy for a theory of action; that with action discourse we have a family of concepts which a theory of action must accommodate. After an exegesis of Davidson's essay "Agency", it is argued that his semantics of action is incompatible with our concepts of motivation and responsibility for action and of attributions of action and agency, and must, therefore, be rejected. A theory of rational agency is presented within which are to be found accounts of intention, coming to intend, intentional action, and an alternative semantics of action which connects the action essentially to agency. The theory of rational agency is then used to illuminate the concepts of trying, compulsion, autonomy and involuntariness, mistake, accident, and the so-called active-passive distinction. / Arts, Faculty of / Philosophy, Department of / Graduate

As technology advances, many practical applications require human-controlled robots. For such applications, it is useful to determine the optimal number of robots an operator should control to maximize human efficiency given different situations. One way to achieve this is through computer simulations of team performance. In order to factor in various parameters that may affect team performance, an agent-based model will be used. Agent-based modeling is a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within an environment [12]. We construct an agent-based model of humans interacting with robots, and explore how team performance relates to different agent parameters and team organizational structures [21]. Prior work describes interaction between a single operator and multiple robots, while this work includes multi-operator performance and coordination. Model parameters include neglect time, interaction time, operator slack time, level of robot autonomy, etc. Understanding the parameters that influence team performance will be a step towards finding ways to maximize performance in real life human-robot systems.

Carta al editor / Revisión por pares

Finding safe trajectories for multiagent autonomous systems can be difficult, especially as multiple robots and obstacles are added to the system. Control barrier functions (CBFs) have been effective in addressing this problem. Although the use of CBFs for guaranteeing safe operation is well established, there is no standard software implementation to simplify the integration of these techniques into robotic systems. We present a CBF Toolbox to fill this void. Although the CBF Toolbox can be used to ensure safety based on local control decisions, it may not be sufficient to guide a robots to their goals in certain environments. In these cases, path planning algorithms are required. We present one such algorithm, which is the multiagent extension of the CBF guided rapidly-exploring random trees (CBF-RRT) to demonstrate how the CBF Toolbox can be applied. This work addresses the theory behind the CBF Toolbox, as well as presenting examples of how it is applied to multiagent systems. Examples are shown for its use in both simulation and hardware experiments. Details are provided on CBF guided rapidly-exploring random trees (CBF-RRT), and its application to multiagent systems with multiagent CBF-RRT (MA-CBF-RRT) that streamlines safe path planning for teams of robots.

Thesis describe theory behind neuroevolution. Then it describes both design and creation of simulated environment for autonomous agent and its training with library Neataptic in environments with various difficulty. Thesis also describes pro- cess of designing frontend for visualization of results and backend for faster training of agents. At the end it describes resulting agents and proposes enhancements to existing solution.

archives@tulane.edu / The patellar tendon transmits loads from the quadriceps to the tibia promoting locomotion. The main etiological factor behind patellar tendinopathies is thought to be excessive loading and unloading during athletic activity (Pearson & Hussain, 2014). The extracellular matrix (ECM) composition and fibroblast-like tenocytes dictate tendon’s uniaxial mechanical properties (Kannus, 2000). Following injury, a flood of inflammatory cells and spike in certain gene expressions work together to remove damaged tissue, trigger fibroblast proliferation, and deposit a provisional collagen matrix (Thomopoulos et al., 2015). Despite these processes, healed tendons demonstrate significant functional deficits (Mienaltowski et al., 2016). Moreover decrease in cell migration and fiber alignment with age further hampers healing outcomes(Dunkman et al., 2013). Efforts to restore tendon function are impeded by a lack of understanding of the early healing process, which may be age- and sex-dependent (Fryhofer et al., 2016; Mienaltowski et al., 2016). The tendon healing process can be further understood using an agent-based model (ABM). ABMs simulate individual agents and the interactions between them and their environment. This approach has the advantage of building complexity from the ground up, mimicking the underlying tendon physiology (Conte & Paolucci, 2014). Therefore, the objectives of this study were to 1) formulate a literature based ABM of murine patellar tendon healing with varying initial conditions to recapitulate changes observed with aging, and 2) Conduct simulations to determine whether ABM recapitulated salient features of healing, and to make predictions about healing outcomes. / 1 / Jordan Robinson

Bibliography: pages 136-144. / This thesis is an attempt to defend explanatory incompatibilism, the view that mechanistic and intentional explanations of behaviour are incompatible, against various sorts of objections which come in the form of rival compatibilist theories. In the first chapter the author outlines the prima facie case for explanatory incompatibilism. This prima facie case is then bolstered by a discussion of explanation in general, conditions of compatibility for different explanations of the same phenomenon, and then a more rigorous account of mechanistic and intentional explanations which allows for a formal presentation of an argument for their incompatibility. Chapters Two, Three and Four discuss some of the combatibilist theories which have been advanced. Chapter Two involves a discussion of the "Double-Language" version of compatibilism as advocated by Ryle and Melden. This version is rejected for two main reasons: (1) it fails to keep the two sorts of explanation sufficiently apart so as to render them compatible, and (2) it fails to show that intentional explanations are not a species of causal explanation. Chapter Three attempts to deal with the "Instrumentalist" version of compatibilism as advanced by Daniel Dennett. This is rejected because it fails to provide a rich enough account of rational action and it also leads to epiphenomenalism. In Chapter Four the author discusses the "Physicalist" approach to the question of compatibility as advocated by Alvin Goldman and Donald Davidson. But this version of compatibilism is found to be wanting because it also leads to the epiphenomenalism of the mental. Chapter Five, the conclusion, summarises the basic argument and attempts to develop the author's own account of what the necessary and sufficient conditions for intentional action are. This is found to involve· three main elements: physical indeterminism, intentional intelligibility, and then something like the concept of agent-causation. In the course of this account there is a brief discussion of the problem of other minds and an argument against the desire-belief model of action and its explanation based on its inability to cope with the problem of deviant causal chains. It is concluded that mechanistic and intentional explanations are indeed incompatible and something is said about the broad metaphysical view which is required to accommodate this fact.

The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered from complex tracking-error dynamics. Furthermore, techniques that are based on linear feedback or output feedback strategies obtained at equilibrium conditions either may not hold or degrade when applied to uncertain dynamic environments. Relying on potential functions, embedded within pre-tuned fuzzy inference architectures, lacks robustness under dynamic disturbances. This thesis introduces two adaptive distributed approaches for the autonomous control of multi-agent systems. The first proposed technique has its structure based on an online fuzzy reinforcement learning Value Iteration scheme which is precise and flexible. This distributed adaptive control system simultaneously targets a number of flocking objectives; namely: 1) tracking the leader, 2) keeping a safe distance from the neighboring agents, and 3) reaching a velocity consensus among the agents. In addition to its resilience in the face of dynamic disturbances, the algorithm does not require more than the agent’s position as a feedback signal. The effectiveness of the proposed method is validated with two simulation scenarios and benchmarked against a similar technique from the literature. The second technique is in the form of an online fuzzy recursive least squares-based Policy Iteration control scheme, which employs a recursive least squares algorithm to estimate the weights in the leader tracking subsystem, as a substitute for the original reinforcement learning actor-critic scheme adopted in the first technique. The recursive least squares algorithm demonstrates a faster approximation weight convergence. The time-invariant communication graph utilized in the fuzzy reinforcement learning method is also improved with time-varying graphs, which can smoothly guide the agents to reach a speed consensus. The fuzzy recursive least squares-based technique is simulated with a few scenarios and benchmarked against the fuzzy reinforcement learning method. The scenarios are simulated in CoppeliaSim for a better visualization and more realistic results.

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