Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

A real-time agent architecture and robust task scheduling.

by Zhao Lei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 78-85).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.ivChapter 1 --- Introduction --- p.1Chapter 2 --- Background --- p.5Chapter 2.1 --- Agents --- p.5Chapter 2.1.1 --- Deliberative Agents --- p.7Chapter 2.1.2 --- Reactive Agents --- p.8Chapter 2.1.3 --- Interacting Agents --- p.9Chapter 2.1.4 --- Hybrid Architectures --- p.10Chapter 2.2 --- Real-time Artificial Intelligence --- p.10Chapter 2.3 --- Real-Time Agents --- p.12Chapter 2.3.1 --- The Subsumption Architecture --- p.13Chapter 2.3.2 --- The InterRAP Architecture --- p.15Chapter 2.3.3 --- The 3T Architecture --- p.16Chapter 2.4 --- On-line Scheduling in Real-Time Agents --- p.18Chapter 3 --- A Real-Time Agent Architecture --- p.20Chapter 3.1 --- Human Cognition Model --- p.20Chapter 3.1.1 --- Perception --- p.22Chapter 3.1.2 --- Cognition --- p.22Chapter 3.1.3 --- Action --- p.23Chapter 3.2 --- Real-Time Message Passing Primitives and Process Structuring --- p.24Chapter 3.2.1 --- Message Passing as IPC --- p.25Chapter 3.2.2 --- Administrator and Worker Processes --- p.28Chapter 3.3 --- Agent Architecture --- p.29Chapter 3.3.1 --- Sensor Workers and the Sensor Administrator --- p.30Chapter 3.3.2 --- The Cognition Workers --- p.32Chapter 3.3.3 --- "The Task Administrator, the Scheduler Worker and Ex- ecutor Workers" --- p.32Chapter 3.4 --- An Agent-Based Real-time Arcade Game --- p.34Chapter 4 --- A Multiple Method Approach to Task Scheduling --- p.37Chapter 4.1 --- Task Scheduling Mechanism --- p.37Chapter 4.1.1 --- Task and Action --- p.38Chapter 4.1.2 --- Task Administrator --- p.40Chapter 4.1.3 --- Task Scheduler --- p.43Chapter 4.2 --- A Task Scheduling Model --- p.44Chapter 4.3 --- Combination Rules and Special Cases --- p.46Chapter 4.4 --- Scheduling Algorithms --- p.49Chapter 5 --- Task Scheduling Model: Analysis and Experiments --- p.53Chapter 5.1 --- Goodness Measure --- p.53Chapter 5.2 --- Theoretical Analysis --- p.54Chapter 5.3 --- Implementation --- p.59Chapter 5.3.1 --- Task Generator Implementation --- p.59Chapter 5.3.2 --- Executor Workers Implementation --- p.61Chapter 5.4 --- Experimental Results --- p.62Chapter 5.4.1 --- Hybrid Mechanism and Individual Algorithms --- p.63Chapter 5.4.2 --- Effect of Average Execution Time --- p.65Chapter 5.4.3 --- Effect of the Greedy Algorithm --- p.65Chapter 5.4.4 --- Effect of the Advanced Algorithm --- p.67Chapter 5.4.5 --- Effect of Actions and Relations Among Them --- p.68Chapter 5.4.6 --- Effect of Deadline --- p.71Chapter 6 --- Conclusions --- p.73Chapter 6.1 --- Summary of Contributions --- p.73Chapter 6.2 --- Future Work --- p.7

Planning with time limits in BDI agent programming language

This paper provides a theoretical basis for performing time limited planning within Belief-Desire-Intention (BDI) agents. The BDI agent architecture is recognised as one of the most popular architectures for developing agents for complex and dynamic environments, in addition to which they have a strong theoretical foundation. Recent work has extended a BDI agent specification language to include HTN-style planning as a built-in feature. However, the extended semantics assume that agents have an unlimited amount of time available to perform planning, which is often not the case in many dynamic real world environments. We extend previous research by using ideas from anytime algorithms, and allow programmer control over the amount of time the agent spends on planning. We show that the resulting integrated agent specification language has advantages over regular BDI agent reasoning

Behavior Trees in Robotics and AI: An Introduction

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion

Automated Negotiation for Provisioning Virtual Private Networks Using FIPA-Compliant Agents

This paper describes the design and implementation of negotiating agents for the task of provisioning virtual private networks. The agents and their interactions comply with the FIPA specification and they are implemented using the FIPA-OS agent framework. Particular attention is focused on the design and implementation of the negotiation algorithms

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Knowledge-Based Task Structure Planning for an Information Gathering Agent

An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed