POLYNOMIAL REGRESSION WITH AUTOMATED DEGREE: A FUNCTION APPROXIMATOR FOR AUTONOMOUS AGENTS

2008 ◽  
Vol 17 (01) ◽  
pp. 159-174 ◽  
Author(s):  
DANIEL STRONGER ◽  
PETER STONE
Keyword(s):  
Approximation Method ◽  
Function Approximation ◽  
Autonomous Agents ◽  
Polynomial Regression ◽  
Training Data ◽  
Autonomous Agent ◽  
Real World Data ◽  
Memory Efficiency ◽  
Empirical Tests ◽  
Function Approximator

In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approximator used by such an agent is subject to a number of constraints that may not apply in a traditional supervised learning setting. Many different function approximators exist and are appropriate for different problems. This paper proposes a set of criteria for function approximators for autonomous agents. Additionally, for those problems on which polynomial regression is a candidate technique, the paper presents an enhancement that meets these criteria. In particular, using polynomial regression typically requires a manual choice of the polynomial's degree, trading off between function accuracy and computational and memory efficiency. Polynomial Regression with Automated Degree (PRAD) is a novel function approximation method that uses training data to automatically identify an appropriate degree for the polynomial. PRAD is fully implemented. Empirical tests demonstrate its ability to efficiently and accurately approximate both a wide variety of synthetic functions and real-world data gathered by a mobile robot.

scholarly journals Identification of mechanical parameters of fiber-reinforced composites by frequency response function approximation method

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987803 ◽  
Author(s):  
Hui Li ◽  
PengCheng Xue ◽  
Wanchong Rong ◽  
XiaoPeng Li ◽  
BangChun Wen
Keyword(s):  
Frequency Response ◽  
Thin Plate ◽  
Response Function ◽  
Approximation Method ◽  
Function Approximation ◽  
Frequency Response Function ◽  
Fiber Reinforced Composites ◽  
Mechanical Parameters ◽  
Reinforced Composites ◽  
Fiber Reinforced

This article proposes frequency response function approximation method to identify mechanical parameters of fiber-reinforced composites. First, a fiber-reinforced composite thin plate is taken as a research object, and its natural characteristic and vibration response under pulse excitation are solved based on the Ritz method and mode superposition method, so that the theoretical calculation of frequency response function of such composite plates can be realized. Then, the identification principle based on frequency response function approximation method is illustrated and its correctness is validated by comparing with other published literature in the verification example, and the specific identification procedure is also proposed. Finally, frequency response function approximation method is applied in a study case, where the elastic moduli, Poisson’s ratios, and loss factors of the TC300 carbon/epoxy composite thin plate are identified, and the influences of boundary conditions, approximation points, total number of modes, and calculation step size on the identification accuracy and efficiency are discussed. It has been proved that the proposed method can identify mechanical parameters of fiber composite materials with high precision and efficiency.

An Autonomous Agent Approach to Query Optimization in Stream Grids

2012 ◽  
pp. 276-299
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

An Autonomous Agent Approach to Query Optimization in Stream Grids

2012 ◽  
pp. 407-428
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

Modulation Scheme for Biasing the Emotional Process of Autonomous Agents

2020 ◽  
pp. 339-361
Author(s):  
Sergio Castellanos ◽  
Luis-Felipe Rodríguez ◽  
J. Octavio Gutierrez-Garcia
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Cognitive Architecture ◽  
Modulation Scheme ◽  
Membership Functions ◽  
Real World Data ◽  
Emotional Process ◽  
Fuzzy Membership Functions ◽  
Cognitive Components ◽  
Cognitive Information

Autonomous agents (AAs) are capable of evaluating their environment from an emotional perspective by implementing computational models of emotions (CMEs) in their architecture. A major challenge for CMEs is to integrate the cognitive information projected from the components included in the AA's architecture. In this chapter, a scheme for modulating emotional stimuli using appraisal dimensions is proposed. In particular, the proposed scheme models the influence of cognition on appraisal dimensions by modifying the limits of fuzzy membership functions associated with each dimension. The computational scheme is designed to facilitate, through input and output interfaces, the development of CMEs capable of interacting with cognitive components implemented in a given cognitive architecture of AAs. A proof of concept based on real-world data to provide empirical evidence that indicates that the proposed mechanism can properly modulate the emotional process is carried out.

scholarly journals Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

2020 ◽  
pp. 001872082096086 ◽  
Author(s):  
Thomas O’Neill ◽  
Nathan McNeese ◽  
Amy Barron ◽  
Beau Schelble
Keyword(s):  
Empirical Research ◽  
High Performance ◽  
Autonomous Agents ◽  
Empirical Literature ◽  
Future Research ◽  
Autonomous Agent ◽  
Research Environments ◽  
Human Autonomy ◽  
Agent Characteristics ◽  
Shared Objective

Objective We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions. Background Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective. Method We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion. Results We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs. Conclusion There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.

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