Reasoning and planning in dynamic domains: An experiment with a mobile robot

Progress made toward having an autonomous mobile robot reason and plan complex tasks in real-world environments is described. To cope with the dynamic and uncertain nature of the world, researchers use a highly reactive system to which is attributed attitudes of belief, desire, and intention. Because these attitudes are explicitly represented, they can be manipulated and reasoned about, resulting in complex goal-directed and reflective behaviors. Unlike most planning systems, the plans or intentions formed by the system need only be partly elaborated before it decides to act. This allows the system to avoid overly strong expectations about the environment, overly constrained plans of action, and other forms of over-commitment common to previous planners. In addition, the system is continuously reactive and has the ability to change its goals and intentions as situations warrant. Thus, while the system architecture allows for reasoning about means and ends in much the same way as traditional planners, it also posseses the reactivity required for survival in complex real-world domains. The system was tested using SRI's autonomous robot (Flakey) in a scenario involving navigation and the performance of an emergency task in a space station scenario

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

Combining Rational and Biological Factors in Virtual Agent Decision Making

To enhance believability of virtual agents, this paper presents an agent-based modelling approach for decision making, which integrates rational reasoning based on means-end analysis with personal psychological and biological aspects. The agent model developed is a combination of a BDI-model and a utility-based decision model in the context of specific desires and beliefs. The approach is illustrated by addressing the behaviour of violent criminals, thereby creating a model for virtual criminals. Within a number of simulation experiments, the model has been tested in the context of a street robbery scenario. In addition, a user study has been performed, which confirms the fact that the model enhances believability of virtual agents. © 2009 The Author(s)

Developing manufacturing control software: A survey and critique

The complexity and diversity of manufacturing software and the need to adapt this software to the frequent changes in the production requirements necessitate the use of a systematic approach to developing this software. The software life-cycle model (Royce, 1970) that consists of specifying the requirements of a software system, designing, implementing, testing, and evolving this software can be followed when developing large portions of manufacturing software. However, the presence of hardware devices in these systems and the high costs of acquiring and operating hardware devices further complicate the manufacturing software development process and require that the functionality of this software be extended to incorporate simulation and prototyping.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45542/1/10696_2005_Article_BF01328739.pd

Implicitly and intelligently influencing the interactive experience

Enabling intuitive interaction in system design remains an art more than a science. This difficulty is exacerbated when the diversity of device and end user group is considered. In this paper, it is argued that conventional interaction modalities are unsuitable in many circumstances and that alternative modalities need be considered. Specifically the case of implicit interaction is considered, and the paper discusses how its use may lead to more satisfactory experiences. Specifically, harnessing implicit interaction in conjunction with the traditional explicit interaction modality, can enable a more intuitive and natural interactive experience. However, the exercise of capturing and interpreting implicit interaction is problematic and is one that lends itself to the adoption of AI techniques. In this position paper, the potential of lightweight intelligent agents is proposed as a model for harmonising the explicit and implicit components of an arbitrary interaction.Science Foundation Ireland12M embargo until January 2012 - AV 10/05/201

Failing Believably: Toward Drama Management with Autonomous Actors in Interactive Narratives

riedl AT ict.usc.edu, andrew AT proceduralarts.com Abstract. Interactive Narrative is an approach to interactive entertainment that enables the player to make decisions that directly affect the direction and/or outcome of the narrative experience being delivered by the computer system. One common interactive narrative technique is to use a drama manager to achieve a specific narrative experience. To achieve character believability, autonomous character agents can be used in conjunction with drama management. In this paper, we describe the problem of failing believably in which character believability and drama management come into conflict and character agents must intelligently produce behaviors that explain away schizophrenic behavior. We describe technologies for implementing semiautonomous believable agents that can fail believably.