Abstract
The goal of my work is to develop and implement an architecture for an autonomous agent, which I refer to as "ANA". An ANA agent consists of a distributed set of "competence modules". Competence modules are linked in a network. A spreading activation process operates on the network to decide what the "relevance" or relative strength of a competence module is in the current context. This process implements a competition among modules for activation energy. The higher the activation energy level of a module, the more likely it is that this module determines what the autonomous agent does or communicates to believe. Learning is a central, completely integrated feature of the architecture. The competence module network is continuously being developed and changed on the basis of experience: links are added and deleted depending on real world observations and new "macro modules" are created whenever a goal is achieved. This paper presents an overview of the architecture. It describes the functionalities that have been implemented, the results that have been obtained with robotic and simulated ANA agents, and finally it discusses (current) limitations and future work.
Keywords
ArchitectureComputer scienceCompetence (human resources)MacroAutonomous agentNetwork architectureAgent architectureDistributed computingHuman–computer interactionComputer architectureSoftware engineeringMulti-agent systemArtificial intelligenceComputer networkPsychology
Affiliated Institutions
Related Publications
A robust layered control system for a mobile robot
A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up...
Counterfactual Multi-Agent Policy Gradients
Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. There is a great...
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting...
Scale-Transferrable Object Detection
Scale problem lies in the heart of object detection. In this work, we develop a novel Scale-Transferrable Detection Network (STDN) for detecting multi-scale objects in images. I...
ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
Current speaker verification techniques rely on a neural network to extract speaker representations. The successful x-vector architecture is a Time Delay Neural Network (TDNN) t...
Publication Info
- Year
- 1991
- Type
- article
- Volume
- 2
- Issue
- 4
- Pages
- 115-120
- Citations
- 171
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
171
OpenAlex
Cite This
Pattie Maes
(1991).
The agent network architecture (ANA).
ACM SIGART Bulletin
, 2
(4)
, 115-120.
https://doi.org/10.1145/122344.122367
Identifiers
- DOI
- 10.1145/122344.122367