Archive for the ‘Robotics’ Category

Emotional Memory and Adaptive Personalities

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 in new environments. Fortunately, research in memory, emotion and personality in humans and animals points to a solution to this problem. Emotions focus an animal’s attention on things it needs to care about, and strong emotions trigger enhanced formation of memory, enabling the animal to adapt its emotional response to the objects and situations in its environment. In humans this process becomes reflective: emotional stress or frustration can trigger re-evaluating past behavior with respect to personal standards, which in turn can lead to setting new strategies or goals.

To aid the authoring of adaptive agents, we present an artificial intelligence model inspired by these psychological results in which an emotion model triggers case-based emotional preference learning and behavioral adaptation guided by personality models. Our tests of this model on robot pets and embodied characters show that emotional adaptation can extend the range and increase the behavioral sophistication of an agent without the need for authoring additional hand-crafted behaviors.

Read the paper:

Emotional Memory and Adaptive Personalities

by Anthony Francis, Manish Mehta, Ashwin Ram

Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, IGI Global, 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-08-10.pdf

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 question: how can we more easily integrate robotic technologies?

We argue that the most effective approach is through standardized interfaces (APIs) to robotic hardware and software technologies (e.g., path planning toolkits). JavaBots is an example framework that provides this kind of integration in simulation and on robot ahrdware. A high-level common interface to sensors and actuators allows control systems to run on multiple simulated and real hardware platforms. Conversely, JavaBots supports the evaluation of competing control systems on the same hardware. In this article, we describe JavaBots and provide examples of robot systems built using it.

Read the paper:

Integrating Robotic Technologies with JavaBots

by Tucker Balch, Ashwin Ram

AAAI Spring Symposium on Integrating Robotic Research: Taking the Next Leap, Stanford, CA, March 1998
www.cc.gatech.edu/faculty/ashwin/papers/er-98-01.pdf

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 applied to systems having continuous states and action spaces is that the value function must operate with a domain consisting of real-valued variables, which means that it should be able to represent the value of infinitely many state and action pairs. For this reason, function approximators are used to represent the value function when a close-form solution of the optimal policy is not available.

In this paper, we extend a previously proposed reinforcement learning algorithm so that it can be used with function approximators that generalize the value of individual experiences across both, state and action spaces. In particular, we discuss the benefits of using sparse coarse-coded function approximators to represent value functions and describe in detail three implementations: CMAC, instance-based, and case-based. Additionally, we discuss how function approximators having different degrees of resolution in different regions of the state and action spaces may influence the performance and learning efficiency of the agent.

We propose a simple and modular technique that can be used to implement function approximators with non-uniform degrees of resolution so that it can represent the value function with higher accuracy in important regions of the state and action spaces. We performed extensive experiments in the double integrator and pendulum swing up systems to demonstrate the proposed ideas.

Read the paper:

Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces

by Juan Santamaria, Rich Sutton, Ashwin Ram

Adaptive Behavior, 6(2):163-217, 1997
www.cc.gatech.edu/faculty/ashwin/papers/er-98-02.pdf

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, but also directors to some degree. Because the number of variables involved in the solution of the dual control problem increases with the number of decision stages, the exact solution of the dual control problem is computationally intractable except for a few special cases.

This paper provides an overview of dual control theory and proposes a heuristic approach towards obtaining a near-optimal dual control method that can be implemented. The proposed algorithm selects control actions taking into account the information contained in past observations as well as the possible information that future observations may reveal. In short, the algorithm anticipates the fact that future learning is possible and selects the control actions accordingly. The algorithm uses memory-based methods to associate long-term benefit estimates to belief states and actions, and selects the actions to execute next according to such estimates. The algorithm uses the outcome of every experience to progressively refine the long-term benefit estimates so that it can make better, improved decisions as it progresses. The algorithm is tested on a classical simulation problem.

Read the paper:

A New Heuristic Approach for Dual Control

by Juan Carlos Santamaria, Ashwin Ram

AAAI-97 Workshop on On-Line Search, Providence, RI, July 1997
www.cc.gatech.edu/faculty/ashwin/papers/er-97-02.pdf

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 continuous adaptation and learning during the performance task.

This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (Self-Improving Navigation System). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.

Read the paper:

Continuous Case-Based Reasoning

by Ashwin Ram, Juan Carlos Santamaria

Artificial Intelligence journal, (90)1-2:25-77, 1997
www.cc.gatech.edu/faculty/ashwin/papers/er-97-06.pdf

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 environments without extensive high-level reasoning that would otherwise slow the system down. The method is implemented in the ACBARR (A Case-BAsed Reactive Robotic) system, and evaluated through empirical simulation of the system on several different environments, including “box canyon” environments known to be problematic for reactive control systems in general.

Read the paper:

Case-Based Reactive Navigation: A Case-Based Method for On-Line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotic Systems

by Ashwin Ram, Ron Arkin, Kenneth Moorman, Russ Clark

IEEE Transactions on Systems, Man, and Cybernetics, 27B(3), 1997. Preliminary version published as Technical Report GIT-CC-92/57, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1992
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-57.pdf

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 paper presents a formulation of learning adaptive reactive controllers. Adaptive reactive controllers inherit all the advantages of traditional reactive controllers, but in addition they are able to adjust themselves to the current operating conditions of the robot and the environment in order to improve task performance. Furthermore, learning adaptive reactive controllers can learn when and how to adapt the reactive controller so as to achieve effective performance under different conditions.

The paper presents an algorithm for a learning adaptive reactive controller that combines ideas from case-based reasoning and reinforcement learning to construct a mapping between the operating conditions of a controller and the appropriate controller configuration; this mapping is in turn used to adapt the controller configuration dynamically. As a case study, the algorithm is implemented in a robotic navigation system that controls a Denning MRV-III mobile robot. The system is extensively evaluated using statistical methods to verify its learning performance and to understand the relevance of different design parameters on the performance of the system.

Read the paper:

Learning Adaptive Reactive Controllers

by Juan Carlos Santamaria, Ashwin Ram

Technical Report GIT-CC-97/05, College of Computing, Georgia Institute of Technology, Atlanta, GA, January 1997
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-97-05.pdf

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 system in terms of the theory and design of the computational model, to select the best system configuration for a given domain, and to predict how the system will behave when the characteristics of the domain or problem change. In addition, for case-based reasoning and other machine learning systems, it is important to evaluate the improvement in the performance of the system with experience (or with learning), to show that this improvement is statistically significant, to show that the variability in performance decreases with experience (convergence), and to analyze the impact of the design decisions on this improvement in performance.

We present a methodology for the evaluation of CBR and other AI systems through systematic empirical experimentation over a range of system configurations and environmental conditions, coupled with rigorous statistical analysis of the results of the experiments. We illustrate this methodology with a case study in which we evaluate a multistrategy case-based and reinforcement learning system which performs autonomous robotic navigation. In this case study, we evaluate a range of design decisions that are important in CBR systems, including configuration parameters of the system (e.g., overall size of the case library, size or extent of the individual cases), problem characteristics (e.g., problem difficulty), knowledge representation decisions (e.g., choice of representational primitives or vocabulary), algorithmic decisions (e.g., choice of adaptation method), and amount of prior experience (e.g., learning or training). We show how our methodology can be used to evaluate the impact of these decisions on the performance of the system and, in turn, to make the appropriate choices for a given problem domain and verify that the system does behave as predicted.

Read the paper:

Systematic Evaluation of Design Decisions in Case-Based Reasoning Systems

by Juan Carlos Santamaria, Ashwin Ram

In Case-Based Reasoning: Experiences, Lessons, and Future Directions, D.B. Leake, editor, AAAI Press, 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-05.pdf

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 objective of this dissertation is to study algorithms by which an autonomous agents can learn, using their own experience, to perform sequential decision-making efficiently and autonomously. The dissertation describes a framework for studying autonomous sequential decision-making consisting of three main elements: the agent, the environment, and the task. The agent attempts to control the environment by perceiving the environment and choosing actions in a sequential fashion. The environment is a dynamic system characterized by a state and its dynamics, a function that describes the evolution of the state given the agent’s actions. A task is a declarative description of the desired behavior the agent should exhibit as it interacts with the environment. The ultimate goal of the agent is to learn a policy or strategy for selecting actions that maximizes its expected benefit as defined by the task.

The dissertation focuses on sequential decision-making when the environment is characterized by continuous states and actions, and the agent has imperfect perception, incomplete knowledge, and limited computational resources. The main characteristic of the approach proposed in this dissertation is that the agent uses its previous experiences to improve estimates of the long-term benefit associated with the execution of specific actions. The agent uses these estimates to evaluate how desirable is to execute alternative actions and select the one that best balances the short- and long-term consequences, taking special consideration of the expected benefit associated with actions that accomplish new learning while making progress on the task.

The approach is based on novel methods that are specifically designed to address the problems associated with continuous domains, imperfect perception, incomplete knowledge, and limited computational resources. The approach is implemented using case-based techniques and extensively evaluated in simulated and real systems including autonomous mobile robots, pendulum swinging and balancing controllers, and other non-linear dynamic system controllers.

Read the thesis:

Learning Adaptive Reactive Agents

by Juan Carlos Santamaria

PhD Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1996
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-97-08.ps.Z

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 control architecture greatly reduces the effort required to configure a navigation system. Unlike standard genetic algorithms, our method uses a floating point gene representation. The system is fully implemented and has been evaluated through extensive computer simulations of robot navigation through various types of environments.

Read the paper:

Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation

by Ashwin Ram, Ron Arkin, Gary Boone, Michael Pearce

Adaptive Behavior, 2(3):277-305, 1994
www.cc.gatech.edu/faculty/ashwin/papers/er-94-01.pdf
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