Planning for Real Time Event Response Management

Overview

  • Real-time intelligent agents: solutions and applications
  • Choosing and integrating the best planning techniques
  • Reaction decision frameworks and language frameworks
  • Includes a complete case study

The act of planning for and responding to real-time events has been an area of intense investigation in the past few years. Planning for Real Time Event Response Management offers ...

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Overview

  • Real-time intelligent agents: solutions and applications
  • Choosing and integrating the best planning techniques
  • Reaction decision frameworks and language frameworks
  • Includes a complete case study

The act of planning for and responding to real-time events has been an area of intense investigation in the past few years. Planning for Real Time Event Response Management offers this state-of-the-art technology in depth and demonstrates how it can be employed for maximum effect. The techniques presented here are applicable to an exceptionally broad range of problems, from web-based shopping assistants to robots, medicine to financial analysis. Coverage includes:

  • Conventional and reactive planning: when to use each, and when to avoid planning
  • A flight planning example used throughout the book to bring together the concepts
  • Key reactive planning techniques such as Brooks' subsumption architecture and Rosenschein/Kaelbling's situated automata—and their limitations
  • Contingencies: building intelligent agents that can handle unanticipated events
  • Applying planning paradigms to real-world domains
  • Building Reaction Decision Frameworks that integrate conventional and reactive planning
  • Solving problems at execution time: anytime algorithms and real-time architectures
  • Real-time planning: integrating artificial intelligence and real-time computing

Planning for Real Time Event Response Management introduces a framework for describing reactive behavior that can be used in many knowledge domains, concluding with a previewof the next steps to be taken in intelligent agent development, including integration of real-time problem solving languages with existing Internet infrastructures.

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Product Details

  • ISBN-13: 9780130951922
  • Publisher: Prentice Hall Professional Technical Reference
  • Publication date: 6/12/2000
  • Edition number: 1
  • Pages: 416
  • Product dimensions: 7.33 (w) x 9.59 (h) x 1.43 (d)

Read an Excerpt

PREFACE:

Introduction

In the course of our daily lives, we are frequently called upon to make decisions under time pressure. For example, if we are driving to work, we want to plan out a trip that will get us there on time. However, it is very difficult to be absolutely certain that we will make it on time. We may be able to plan for the possibility of heavier-than-usual traffic and allow a little extra time. Or we may be able to consider the possibility of a flat tire and still be able to make it to work on time. We may allow for these possibilities by planning out alternate routes in advance, or simply by responding to the events as they arise, or through some combination of these approaches.

However, it is very difficult for us to guarantee absolutely that we will get to work under all possible scenarios. Some scenarios may be too unlikely to be worth planning for-for example, an earthquake shutting down all major streets on the way. Some scenarios may be too costly to prepare for-we may, for example, estimate that the cost of a bulletproof car is not justified given the relatively low risk of being shot in a particular neighborhood. We have to make trade-offs. And not only do we have to make trade-offs, we have to make the trade-offs quickly-we don't have all day to plan out the optimal way for getting to work.

Once we get to work, we will likely be faced with a new set of problems that require us to make trade-offs based on real-time constraints. We are expected to reach a deadline on a particular project. However, meeting that deadline requires that we know when different resources will be available to us. What if a key project member resigns? Dowe have a contingency plan for dealing with this? Should we spend some time planning for such eventualities? What if the time we spend planning begins to slow down the project?

Then, we fly to a business meeting in another city, and the pilot in charge of the flight has to deal with real-time considerations. Does he have enough fuel on board to get to the destination airport? Does he have enough fuel on board to get to a different airport if the destination airport is unexpectedly closed? Does he have enough fuel on board to get to every possible airport that a terrorist might want to hijack the plane to? Again, there are many real-time considerations. Faced with all this stress, perhaps we end up in the intensive care unit where the physician in charge will have yet another set of real-time constraints that he or she has to take into account. And it goes on and on.

These are the types of considerations that we as modern human beings face in our daily lives. And, as more and more aspects of our lives become automated, these are considerations that software is also going to need to become aware of and capable of handling in an effective manner. Some of these problems could possibly be "solved" statistically if we had enough data and time. If such resources were available, it would be worth doing so. However, human beings routinely have to make real-time decisions without anything even close to complete statistical information. Software will need to be capable of doing the same.

What this book shows are a number of techniques by which we can make these real-time choices in an intelligent manner. Certainly, it is intended that these approaches can be applied by the designer of a software system in solving real-time problems automatically. There is also another, pedagogical application of the ideas presented in this book. As humans begin to understand how to solve real-time problems automatically, they can look back at their own processes and see whether they are being solved in the most efficient way possible. Such an analysis will likely lead either to greater efficiency in human processes, or else to a better understanding of how humans solve problems effectively in real time, which will lead to better software engineering.

A concept that appears throughout this book is that of an intelligent agent. An agent is anything that can, through its interaction with the environment, change the state of that environment. Examples of agents are: humans, other live beings, computer programs-any objects that in any way interact with their environment. An intelligent agent is an agent, human or otherwise, that exhibits a behavior that would be considered evidence of intelligence.

Note that such behavior need not be optimal for a specific domain or situation in order to be considered intelligent. A more restrictive definition of intelligent agent may require that the agent behaves in the same way as a human being with knowledge in that specific domain. Unless otherwise specified, we will use this more restrictive definition when discussing an agent's behavior in a restricted or specialized domain.

In all these cases, the principal or agent (human or software) must make decisions to optimize the outcome and the use of resources. It will therefore exhibit what is commonly referred to as intelligent behavior.

An intelligent agent needs to be able to respond effectively in real time in order to operate in most real-world domains. We should, right at the outset, be clear on what we mean by "respond effectively in real time," since the term real time is used in different contexts to mean different things. By real time, we mean that there is some type of deadline, or at least degradation in the utility of the agent's response, so that fast response is not only desirable but required. Effective response by an intelligent agent means that the agent is capable of making trade-offs of some type in order to meet the deadline.

An example may help to illustrate what we do-and do not-consider to be "real-time response." There are many software programs running on the World Wide Web; these programs are often characterized as real-time because they are able to respond to the click of a button. However, we shouldn't consider these programs to be real-time because their responses to real-time constraints are not intelligent. Most programs on the Web allow the user to click a button, but then the user is left at the mercy of the system as to how long the response takes. Often it is very fast, but sometimes the user is left hanging for many seconds or minutes. Rarely is anything intelligent done in response to such a delay. An intelligent trade-off might be, for example, to offer the user the chance to receive the response by email later if it isn't available in under thirty seconds. This would free the user to do other tasks, while still providing a response of some utility.

It is common as we move into the new millennium to think of intelligent agents as being necessarily deployed on the World Wide Web. While the previous example shows that the Web is certainly one place where an intelligent agent can be deployed, we think of agents in a broader context. For example, consider the problem of a parachutist who perceives a delay in a parachute opening. The parachutist has a couple of tests that he or she can perform to determine the nature of the problem. She or he can look up at the partially opened parachute to attempt to determine the nature of the problem, or he or she can look at the altimeter for a few seconds to determine the rate of descent. However, the parachutist has only a limited amount of time in which to respond before hitting "ground zero," so the parachutist may need to act with less than complete information. From our point of view, the parachutist is functioning as an intelligent agent.

The ideas presented in this book are intended to be applicable to any type of intelligent agent. An agent can be a software program (an "intelligent software agent"), a robot, a human being, or virtually anything else capable of responding to its environment. The purpose of this book is to show how to handle trade-offs of the types required in the Web and parachuting examples. To what extent is planning required? How should the agent deal with unexpected contingencies that may interfere with executing a plan? Should the agent be prepared to deal with all possible contingencies, with those most likely, or with those that have the most negative consequences if action is not taken?

This book presents state of the art real-time event response management. We will describe a variety of approaches that are available to solve problems such as the ones we have just outlined. We describe in which types of situations the different approaches are likely to be useful, and we leave it to the reader to decide which particular approaches to apply to his or her individual problem.

This book will be of interest to anyone designing a system capable of functioning in a real-time, dynamic domain. Software managers developing software in real-time domains-in medicine, aviation, finance, transportation, nuclear power plant safety, engineering, Web applications, and many others-will be able to use these approaches to develop more intelligent systems. Higher levels of management, such as department managers and CIOs, will also find this book useful, as the real-time nature of many problems often impacts design decisions at the highest levels. Project managers who work extensively with software systems will also benefit from this book, as the real-time nature of many business problems and the incomplete nature of the information available at decision time often impacts on both the software and the human intelligent agents in a business environment.

Software managers will probably benefit most from the technical sections of the book, in particular Chapters 2 through 6. They may also be interested in chapter 1, although they may find this too high a level. Higher levels of management will probably benefit most from Chapter 1, as well as Chapter 8, which give a high-level overview without too much technical detail. Project managers who work with software systems will probably have a similar interest as software managers, although they may be able to skip sections 2.1 through 2.4, and 6.2 through 6.3.
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Table of Contents

1. Evolution of Planning.
A Brief History of Planning. Types of Domains Used In AI. Conventional Planners. Pure (Hard-Wired) Reaction. Classical Real-time Planners. Book Synopsis.

2. Subsuming Planning to Reaction.
Minton's Utility Function. Insect Intelligence and Robotics: The Work of Rodney Brooks. Procedural Reasoning Systems. Situated Automata and Situated Agents. Disadvantages and Next Step.

3. Integration of Planning and Reaction.
The Planning to Reaction Spectrum. The Contingency Space. The Need for Deciding Between Planning and Reaction.

4. Deciding at Planning Time.
Alternative Approaches. Empirical Approach. Analytical Approach. The Need for Good Real-time Components.

5. Execution Time: Reacting, (Re)Planning and Deciding.
Anytime Algorithms. Action-based Decision Trees.

6. Real-time Planning.
Guaranteeing Temporal Adequacy. Building Guaranteed And Unguaranteed Plans.

7. Applications.
A Language Framework for Describing Reactive Behaviors. Formalizing Reaction Decisions in Critical Domains for Pedagogical Purposes.

8. Conclusion.
Integration of the Ideas. Language Development. Integration with Existing Technologies. Development of Hardware.

Index.
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Preface

PREFACE:

Introduction

In the course of our daily lives, we are frequently called upon to make decisions under time pressure. For example, if we are driving to work, we want to plan out a trip that will get us there on time. However, it is very difficult to be absolutely certain that we will make it on time. We may be able to plan for the possibility of heavier-than-usual traffic and allow a little extra time. Or we may be able to consider the possibility of a flat tire and still be able to make it to work on time. We may allow for these possibilities by planning out alternate routes in advance, or simply by responding to the events as they arise, or through some combination of these approaches.

However, it is very difficult for us to guarantee absolutely that we will get to work under all possible scenarios. Some scenarios may be too unlikely to be worth planning for-for example, an earthquake shutting down all major streets on the way. Some scenarios may be too costly to prepare for-we may, for example, estimate that the cost of a bulletproof car is not justified given the relatively low risk of being shot in a particular neighborhood. We have to make trade-offs. And not only do we have to make trade-offs, we have to make the trade-offs quickly-we don't have all day to plan out the optimal way for getting to work.

Once we get to work, we will likely be faced with a new set of problems that require us to make trade-offs based on real-time constraints. We are expected to reach a deadline on a particular project. However, meeting that deadline requires that we know when different resources will be available to us. What if a key project member resigns?Dowe have a contingency plan for dealing with this? Should we spend some time planning for such eventualities? What if the time we spend planning begins to slow down the project?

Then, we fly to a business meeting in another city, and the pilot in charge of the flight has to deal with real-time considerations. Does he have enough fuel on board to get to the destination airport? Does he have enough fuel on board to get to a different airport if the destination airport is unexpectedly closed? Does he have enough fuel on board to get to every possible airport that a terrorist might want to hijack the plane to? Again, there are many real-time considerations. Faced with all this stress, perhaps we end up in the intensive care unit where the physician in charge will have yet another set of real-time constraints that he or she has to take into account. And it goes on and on.

These are the types of considerations that we as modern human beings face in our daily lives. And, as more and more aspects of our lives become automated, these are considerations that software is also going to need to become aware of and capable of handling in an effective manner. Some of these problems could possibly be "solved" statistically if we had enough data and time. If such resources were available, it would be worth doing so. However, human beings routinely have to make real-time decisions without anything even close to complete statistical information. Software will need to be capable of doing the same.

What this book shows are a number of techniques by which we can make these real-time choices in an intelligent manner. Certainly, it is intended that these approaches can be applied by the designer of a software system in solving real-time problems automatically. There is also another, pedagogical application of the ideas presented in this book. As humans begin to understand how to solve real-time problems automatically, they can look back at their own processes and see whether they are being solved in the most efficient way possible. Such an analysis will likely lead either to greater efficiency in human processes, or else to a better understanding of how humans solve problems effectively in real time, which will lead to better software engineering.

A concept that appears throughout this book is that of an intelligent agent. An agent is anything that can, through its interaction with the environment, change the state of that environment. Examples of agents are: humans, other live beings, computer programs-any objects that in any way interact with their environment. An intelligent agent is an agent, human or otherwise, that exhibits a behavior that would be considered evidence of intelligence.

Note that such behavior need not be optimal for a specific domain or situation in order to be considered intelligent. A more restrictive definition of intelligent agent may require that the agent behaves in the same way as a human being with knowledge in that specific domain. Unless otherwise specified, we will use this more restrictive definition when discussing an agent's behavior in a restricted or specialized domain.

In all these cases, the principal or agent (human or software) must make decisions to optimize the outcome and the use of resources. It will therefore exhibit what is commonly referred to as intelligent behavior.

An intelligent agent needs to be able to respond effectively in real time in order to operate in most real-world domains. We should, right at the outset, be clear on what we mean by "respond effectively in real time," since the term real time is used in different contexts to mean different things. By real time, we mean that there is some type of deadline, or at least degradation in the utility of the agent's response, so that fast response is not only desirable but required. Effective response by an intelligent agent means that the agent is capable of making trade-offs of some type in order to meet the deadline.

An example may help to illustrate what we do-and do not-consider to be "real-time response." There are many software programs running on the World Wide Web; these programs are often characterized as real-time because they are able to respond to the click of a button. However, we shouldn't consider these programs to be real-time because their responses to real-time constraints are not intelligent. Most programs on the Web allow the user to click a button, but then the user is left at the mercy of the system as to how long the response takes. Often it is very fast, but sometimes the user is left hanging for many seconds or minutes. Rarely is anything intelligent done in response to such a delay. An intelligent trade-off might be, for example, to offer the user the chance to receive the response by email later if it isn't available in under thirty seconds. This would free the user to do other tasks, while still providing a response of some utility.

It is common as we move into the new millennium to think of intelligent agents as being necessarily deployed on the World Wide Web. While the previous example shows that the Web is certainly one place where an intelligent agent can be deployed, we think of agents in a broader context. For example, consider the problem of a parachutist who perceives a delay in a parachute opening. The parachutist has a couple of tests that he or she can perform to determine the nature of the problem. She or he can look up at the partially opened parachute to attempt to determine the nature of the problem, or he or she can look at the altimeter for a few seconds to determine the rate of descent. However, the parachutist has only a limited amount of time in which to respond before hitting "ground zero," so the parachutist may need to act with less than complete information. From our point of view, the parachutist is functioning as an intelligent agent.

The ideas presented in this book are intended to be applicable to any type of intelligent agent. An agent can be a software program (an "intelligent software agent"), a robot, a human being, or virtually anything else capable of responding to its environment. The purpose of this book is to show how to handle trade-offs of the types required in the Web and parachuting examples. To what extent is planning required? How should the agent deal with unexpected contingencies that may interfere with executing a plan? Should the agent be prepared to deal with all possible contingencies, with those most likely, or with those that have the most negative consequences if action is not taken?

This book presents state of the art real-time event response management. We will describe a variety of approaches that are available to solve problems such as the ones we have just outlined. We describe in which types of situations the different approaches are likely to be useful, and we leave it to the reader to decide which particular approaches to apply to his or her individual problem.

This book will be of interest to anyone designing a system capable of functioning in a real-time, dynamic domain. Software managers developing software in real-time domains-in medicine, aviation, finance, transportation, nuclear power plant safety, engineering, Web applications, and many others-will be able to use these approaches to develop more intelligent systems. Higher levels of management, such as department managers and CIOs, will also find this book useful, as the real-time nature of many problems often impacts design decisions at the highest levels. Project managers who work extensively with software systems will also benefit from this book, as the real-time nature of many business problems and the incomplete nature of the information available at decision time often impacts on both the software and the human intelligent agents in a business environment.

Software managers will probably benefit most from the technical sections of the book, in particular Chapters 2 through 6. They may also be interested in chapter 1, although they may find this too high a level. Higher levels of management will probably benefit most from Chapter 1, as well as Chapter 8, which give a high-level overview without too much technical detail. Project managers who work with software systems will probably have a similar interest as software managers, although they may be able to skip sections 2.1 through 2.4, and 6.2 through 6.3.
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