Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while maintaining a consistent personality. For short-term interactions in restricted environments, scripting and state machine techniques can create agents with emotion and personality, but these methods are labor intensive, hard to extend, and brittle [...]
Archive for the ‘Robotics’ Category
23 Mar
Integrating Robotic Technologies with JavaBots
Mobile robotics research advances through developments in theory, and implementation in hardware and software. While theory is important, this article is primarily concerned with hardware and software technologies. It is our view that significant strides can be made just be combining existing hardware and software tools. Thus the focus of this paper is answering the [...]
1 Sep
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
A key element in the solution of reinforcement learning problems is the value function. The purpose of this function is to measure the long-term utility or value of any given state. The function is important because an agent can use this measure to decide what to do next. A common problem in reinforcement learning when [...]
28 Jul
A New Heuristic Approach for Dual Control
Autonomous agents engaged in a continuous interaction with an incompletely known environment face the problem of dual control (Feldbaum, 1965). Simply stated, actions are necessary not only for studying the environment, but also for making progress on the task. In other words, actions must bear a “dual” character: They must be investigators to some degree, [...]
1 Feb
Continuous Case-Based Reasoning
Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and [...]
1 Jan
Case-Based Reactive Navigation: A Case-Based Method for On-Line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotic Systems
This article presents a new line of research investigating on-line learning mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modification of behavior assemblages for a navigational system. The case-based reasoning module is designed as an addition to a traditional reactive control system, and provides more flexible performance in novel [...]
1 Jan
Learning Adaptive Reactive Controllers
Reactive controllers has been widely used in mobile robots since they are able to achieve successful performance in real-time. However, the configuration of a reactive controller depends highly on the operating conditions of the robot and the environment; thus, a reactive controller configured for one class of environments may not perform adequately in another. This [...]
13 Aug
Systematic Evaluation of Design Decisions in Case-Based Reasoning Systems
Two important goals in the evaluation of artificial intelligence systems are to assess the merit of alternative design decisions in the performance of an implemented computer system and to analyze the impact in the performance when the system faces problem domains with different characteristics. Achieving these objectives enables us to understand the behavior of the [...]
15 May
Learning Adaptive Reactive Agents
An autonomous agent is an intelligent system that has an ongoing interaction with a dynamic external world. It can perceive and act on the world through a set of limited sensors and effectors. Its most important characteristic is that it is forced to make decisions sequentially, one after another, during its entire “life”. The main [...]
1 Jan
Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation
This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach evolves reactive control systems in various environments, thus creating sets of “ecological niches” that can be used in similar environments. The use of genetic algorithms as an unsupervised learning method for a reactive [...]
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