Drive-Based Utility-Maximizing Computer Game Non-Player Characters

This research examines the emergence of the five-string fiddle in contemporary North American fiddle culture within the past ten years. By interacting with leading artistlevel practitioners, the research documents the evolution and impact of the instrument to date in exploring the possibilities the five-string fiddle presents for musical performance and innovation. North American vernacular music and, in particular, the contemporary fiddle playing landscape, exemplifies virtousic and innovative idiomatic technique and improvisation as central to an overarching musical explosion, evidenced in the music of many high level, multi-stylistic contemporary practitioners. Within contemporary American fiddle performance, it is compelling to observe how many of the most innovative and highly regarded players now perform on five-string fiddles. The research uses a qualitative research methodology, drawing on interviews conducted with seven leading American fiddle players, each of whom has adopted the five-string fiddle in their own musical practice. The participants represent a rich cross section of American fiddle culture. They emerged naturally during the course of the literature review, and in-depth listening research, as particularly relevant sample cases. All participants were identified as leading exponents of the diversities encompassed in American fiddle music, between them sharing extensive professional recording, performance and academic experience, and all playing on five-string instruments. The research is further illuminated through practice, reflecting on my own musical work in illustrating how I have personally adopted the five-string fiddle, drawing influence from the research in demonstrating some wider possibilities of the instrument. This enquiry is important as it addresses the lack of specific research to date regarding the five-string fiddle, despite the significanance it holds for some of American fiddle music\u27s leading exponents, and consequently, for fiddle music itself. Equally significant, is the role of the instrument in facilitating the performance of innovative extended instrumental techniques, in particular, the five-string fiddles association with the rhythmic/percussive \u27chop\u27 bow techniques, now, so conspicuous within contemporary groove-based American string music. ix The findings of this research established the definitive emergence of the five-string fiddle, and subscribe that the five-string has now become a widely accepted part of the mainstream instrumentation in American music. This understanding emerges clearly through the words and practice of the participants. From this perspective, the research identifies the musical reasons that inspire the instruments popularity and elaborates through practice, the musical possibilities it presents to others. behaviour selection systems that have been used successfully in industry. The evaluations show that UDGOAP can outperform these systems in both environments. Another novel contribution of this thesis is smart ambiance. Smart ambiance is an area of space in a virtual world that holds information about the context of that space and uses this information to have non-player characters inside the space select more contextually appropriate actions. Information about the context comes from events that took place inside the smart ambiance, objects inside the smart ambiance, and the location of the smart ambiance. Smart ambiance can be used with any cost based planner. This thesis demonstrates dierent aspects of smart ambiance by causing an industry standard action planner to select more contextually appropriate behaviours than it otherwise would have without the smart ambiance

Reasoning about Goal-Plan Trees in Autonomous Agents: Development of Petri net and Constraint-Based Approaches with Resulting Performance Comparisons

Multi-agent systems and autonomous agents are becoming increasingly important in current computing technology. In many applications, the agents are often asked to achieve multiple goals individually or within teams where the distribution of these goals may be negotiated among the agents. It is expected that agents should be capable of working towards achieving all its currently adopted goals concurrently. However, in doing so, the goals can interact both constructively and destructively with each other, so a rational agent must be able to reason about these interactions and any other constraints that may be imposed on them, such as the limited availability of resources that could affect their ability to achieve all adopted goals when pursuing them concurrently. Currently, agent development languages require the developer to manually identify and handle these circumstances. In this thesis, we develop two approaches for reasoning about the interactions between the goals of an individual agent. The first of these employs Petri nets to represent and reason about the goals, while the second uses constraint satisfaction techniques to find efficient ways of achieving the goals. Three types of reasoning are incorporated into these models: reasoning about consumable resources where the availability of the resources is limited; the constructive interaction of goals whereby a single plan can be used to achieve multiple goals; and the interleaving of steps for achieving different goals that could cause one or more goals to fail. Experimental evaluation of the two approaches under various different circumstances highlights the benefits of the reasoning developed here whilst also identifying areas where one approach provides better results than the other. This can then be applied to suggest the underlying technique used to implement the reasoning that the agent may want to employ based on the goals it has been assigned

Maintenance goals in intelligent agents

One popular software development strategy is that of intelligent agent systems. Agents are often programmed by goals; a programmer or user defines a set of goals for an agent, and then the agent is left to determine how best to complete the goals assigned to them. Popular types of goals are achievement and maintenance goals. An achievement goal describes some particular state the agent would like to bring about, for example, being in a particular location or having a particular bank balance. Given an achievement goal, an agent will perform actions that it believes will lead it to having the achievement goal realised. In current agent systems, maintenance goals tell an agent to ensure that some condition is always kept satisfied, for example, ensuring that a vehicle stays below a certain speed, or that it has sufficient fuel in its fuel tank. Currently, maintenance goals are reactive, in that they are not considered until after the maintenance condition has been violated. Only then does the agent begin to perform actions to restore the maintenance condition. In this thesis, we have discussed methods by which maintenance goals can be made proactive. Proactive maintenance goals may cause an agent to perform actions before a maintenance condition is violated, when it can predict that a maintenance condition will be violated in the future. This can be due to changes to the environment, or more interestingly, when the agent itself is performing actions that will cause the violation of the maintenance condition. Operational semantics that clearly demonstrate the functionality and operation of proactive maintenance goals have been developed in this thesis. We have experimentally shown that agents with proactive maintenance goals will reduce the amount of resources consumed in a variety of error-prone environments. This includes scenarios where the agent's beliefs are less than the true values, as well as when the beliefs are in excess of the true values

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

Motivations as an abstraction of meta-level reasoning

Abstract. In agent systems, meta-level reasoning is commonly used in enforcing rationality in the choice of goals and actions performed by an agent, ensuring that an agent behaves as effectively and efficiently as possible. Through metareasoning an agent is able to explicitly consider goals before committing to them, and consider courses of action before executing plans. In this paper, we argue that although seldom considered, a flexible meta-level reasoning component is a valuable addition to any agent architecture. We describe such a component for use in BDI architectures, underpinned by a model of motivation and a motivationbased description language, and demonstrate its effectiveness empirically.