Planificación continua: análisis de estrategias de tratamiento de metas

El proyecto de investigación Agentes inteligentes en ambientes dinámicos que esta financiado por la Universidad Nacional del Comahue, tiene como objetivo general el estudio y desarrollo de técnicas de Inteligencia Artificial para dotar de inteligencia y conocimiento a agentes inmersos en mundos virtuales, interactivos y dinámicos. El proyecto tiene diferentes líneas de investigación entre las que se encuentran planificación, tecnologías del lenguaje humano, ingeniería de conocimiento y juegos. El presente trabajo se enmarca en la línea planificación. El objetivo final es desarrollar un módulo de tratamiento y gestión de metas para maximizar las características de autonomía de un agente planificador en un ambiente dinámico.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Tratamiento de metas en planificación continua

El proyecto de investigación Agentes inteligentes en ambientes dinámicos tiene como objetivo general el estudio y desarrollo de técnicas de Inteligencia Artificial para dotar de inteligencia y conocimiento a agentes inmersos en mundos virtuales, interactivos y dinámicos. Diferentes líneas de investigación confluyen en este proyecto. Entre ellas se encuentran planificación, tecnologías del lenguaje humano, ingeniería de conocimiento y juegos. El presente trabajo se enmarca en la línea planificación. El objetivo final es desarrollar un módulo de tratamiento y gestión de metas para maximizar las características de autonomía de un agente planificador en un ambiente dinámico.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Tratamiento de metas en planificación continua

El proyecto de investigación Agentes inteligentes en ambientes dinámicos tiene como objetivo general el estudio y desarrollo de técnicas de Inteligencia Artificial para dotar de inteligencia y conocimiento a agentes inmersos en mundos virtuales, interactivos y dinámicos. Diferentes líneas de investigación confluyen en este proyecto. Entre ellas se encuentran planificación, tecnologías del lenguaje humano, ingeniería de conocimiento y juegos. El presente trabajo se enmarca en la línea planificación. El objetivo final es desarrollar un módulo de tratamiento y gestión de metas para maximizar las características de autonomía de un agente planificador en un ambiente dinámico.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective

We represent planning as a set of loosely coupled network flow problems, where each network corresponds to one of the state variables in the planning domain. The network nodes correspond to the state variable values and the network arcs correspond to the value transitions. The planning problem is to find a path (a sequence of actions) in each network such that, when merged, they constitute a feasible plan. In this paper we present a number of integer programming formulations that model these loosely coupled networks with varying degrees of flexibility. Since merging may introduce exponentially many ordering constraints we implement a so-called branch-and-cut algorithm, in which these constraints are dynamically generated and added to the formulation when needed. Our results are very promising, they improve upon previous planning as integer programming approaches and lay the foundation for integer programming approaches for cost optimal planning

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

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

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Planning with goal utility dependencies

Work in partial satisfaction planning (PSP) has hither to assumed that goals are independent. This implies that that individual goals have additive utility values. In many real-world problems we cannot make this assumption and thus goal utility is not additive. In this paper, we motivate the need for representing and handling goal utility dependencies in PSP and we provide a framework for representing them using the General Additive Independence (GAI) model (Bacchus & Grove 1995). We then present an algorithm based on forward heuristic planning to solve this problem using heuristics derived from the planning graph. To show the effectiveness of our framework, we provide empirical results on benchmark planning domains