Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Artificial Intelligence and Natural Language Processing
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

3326 results in Artificial Intelligence and Natural Language Processing

Page 56 of 167

  • 4 - Reasoning with Constraints

  • from Part II - Reasoning, Planning and Learning with Certainty
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 125-172

  • Summary

    Every task involves constraint,

    Solve the thing without complaint;

    There are magic links and chains

    Forged to loose our rigid brains.

    Structures, strictures, though they bind,

    Strangely liberate the mind.

    – James Falen

    Instead of reasoning explicitly in terms of states, it is typically better to describe states in terms of features and to reason in terms of these features. Features are described using variables. Often features are not independent and there are hard constraints that specify legal combinations of assignments of values to variables. As Falen's elegant poem emphasizes, the mind discovers and exploits constraints to solve tasks. In planning and scheduling an agent assigns a time for each action. These assignments must satisfy constraints on the order actions can be carried out and constraints specifying that the actions achieve a goal. Preferences over assignments are specified in terms of soft constraints. This chapter shows how to generate assignments that satisfy hard constraints and optimize soft constraints.

    PossibleWorlds, Variables, and Constraints

    Variables and Worlds

    Constraint satisfaction problems are described in terms of variables and possible worlds. A possible world is a possible way the world (the real world or some imaginary world) could be.

    Possible worlds are described by algebraic variables. An algebraic variable is a symbol used to denote features of possible worlds. For this chapter, we refer to an algebraic variable simply as a variable. Algebraic variables are written starting with an upper-case letter. Each algebraic variable X has an associated domain, dom(X), which is the set of values the variable can take.

    A discrete variable is one whose domain is finite or countably infinite. A binary variable is a discrete variable with two values in its domain. One particular case of a binary variable is a Boolean variable, which is a variable with domain ﹛true, false﹜. We can also have variables that are not discrete; for example, a variable whose domain corresponds to the real line or an interval of the real line is a continuous variable.

  • 9 - Planning with Uncertainty

  • from Part III - Reasoning, Learning and Acting with Uncertainty
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 425-486

  • Summary

    A plan is like the scaffolding around a building. When you're putting up the exterior shell, the scaffolding is vital. But once the shell is in place and you start to work on the interior, the scaffolding disappears. That's how I think of planning. It has to be sufficiently thoughtful and solid to get the work up and standing straight, but it cannot take over as you toil away on the interior guts of a piece. Transforming your ideas rarely goes according to plan.

    – Twyla Tharp [2003]

    In the quote above, Tharp is referring to dance, but the same idea holds for any agent when there is uncertainty. An agent cannot just plan a fixed sequence of steps; the result of planning needs to be more sophisticated. Planning must take into account the fact that an agent in the real world does not know what will actually happen when it acts. An agent should plan to react to its environment.

    What an agent should do at any time depends on what it will do in the future. When an agent cannot precisely predict the effects of its actions, what it will do in the future depends on what it does now and what it will observe before it acts.

    With uncertainty, an agent typically cannot guarantee to satisfy its goals, and even trying to maximize the probability of achieving a goal may not be sensible. For example, an agent whose goal is to minimize the probability of injury in a car accident would not get into a car or walk down a sidewalk or even go to the ground floor of a building, each of which increases the probability of being injured in a car accident, however slightly. An agent that does not guarantee to satisfy a goal can fail in many ways, some of which may be much worse than others.

    This chapter is about how to take planning, reacting, observing, succeeding and failing into account simultaneously.

    An agent's decision on what to do depends on three things:

  • • the agent's ability. The agent has to select from the options available to it.

  • 3 - Searching for Solutions

  • from Part II - Reasoning, Planning and Learning with Certainty
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 77-124

  • Summary

    Have you ever watched a crab on the shore crawling backward in search of the Atlantic Ocean, and missing? That's the way the mind of man operates.

    – H. L. Mencken (1880–1956)

    The previous chapter discussed how an agent perceives and acts, but not how its goals affect its actions. An agent could be programmed to act in the world to achieve a fixed goal or set of goals, but then it would not adapt to changing goals, and so would not be intelligent. An intelligent agent needs to reason about its abilities and its goals to determine what to do. This chapter casts the problem of an agent deciding how to solve a goal as the problem of searching to find a path in a graph. It presents a number of ways to solve such problems on a computer.

    Problem Solving as Search

    In the simplest case of an agent deciding what it should do, the agent has a state-based model of the world, with a goal to achieve and no uncertainty. This is either a flat (non-hierarchical) representation or a single level of a hierarchy. The agent is able to determine how to achieve its goal by searching in its representation of the world state space for a way to get from its current state to a state that satisfies its goal. Given a complete model, it tries to find a sequence of actions that will achieve its goal before it has to act in the world.

    This problem can be abstracted to the mathematical problem of finding a path from the start node to a goal node in a directed graph. Many other problems can also be mapped to this abstraction, so it is worthwhile to consider this level of abstraction. Most of this chapter explores various algorithms for finding such paths.

    Example 3.1 Computer maps provide path-finding: showing how to drive (or ride, walk or take transit) from one location to another. Finding the best route from a current location to a destination is a search problem. The state includes the location, and possibly the driving direction and speed. A legal route will include the roads (going the correct way down one-way streets) and intersections the traveler will traverse.

  • 7 - Supervised Machine Learning

  • from Part II - Reasoning, Planning and Learning with Certainty
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 267-340

  • Summary

    Who so neglects learning in his youth, loses the past and is dead for the future.

    – Euripides (484 BC – 406 BC), Phrixus, Frag. 927

    Learning is the ability of an agent to improve its behavior based on experience. This could mean the following

  • • The range of behaviors is expanded; the agent can do more.

  • • The accuracy on tasks is improved; the agent can do things better.

  • • The speed is improved; the agent can do things faster.

    • The ability to learn is essential to any intelligent agent. As Euripides pointed out, learning involves an agent remembering its past in a way that is useful for its future.

    This chapter considers the problem of making a prediction as supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new example where only the inputs are given. We explore four approaches to learning: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or (in Section 10.4 (page 512)) predicting based on the posterior probability distribution of hypotheses conditioned on the training examples.

    Chapter 10 considers learning probabilistic models. Chapter 12 covers reinforcement learning. Section 15.2 (page 701) considers learning relational representations.

    Learning Issues

    The following components are part of any learning problem:

    Task The behavior or task that is being improved

    Data The experiences that are used to improve performance in the task, usually in the form of a sequence of examples

    Measure of improvement How the improvement is measured – for example, new skills that were not present initially, increasing accuracy in prediction, or improved speed

    Consider the agent internals of Figure 2.9 (page 66). The problem of learning is to take in prior knowledge and data (e.g., about the experiences of the agent) and to create an internal representation (the knowledge base) that is used by the agent as it acts.

    Learning techniques face the following issues:

    Task Virtually any task for which an agent can get data or experiences can be learned.

  • 14 - Ontologies and Knowledge-Based Systems

  • from Part IV - Reasoning, Learning and Acting with Individuals and Relations
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 645-690

  • Summary

    The most serious problems standing in the way of developing an adequate theory of computation are as much ontological as they are semantical. It is not that the semantic problems go away; they remain as challenging as ever. It is just that they are joined – on center stage, as it were – by even more demanding problems of ontology.

    – Smith [1996, p. 14]

    How do you represent knowledge about a world to make it easy to acquire, debug, maintain, communicate, share, and reason with that knowledge? This chapter explores how to specify the meaning of symbols in intelligent agents, how to use the meaning for knowledge-based debugging and explanation, and, finally, how an agent can represent its own reasoning and how this may be used to build knowledge-based systems. As Smith points out in the quote above, the problems of ontology are central for building intelligent computational agents.

    Knowledge Sharing

    Having an appropriate representation is only part of the story of building a knowledge-based agent. We also should ensure that the knowledge can be acquired from people and from data. The knowledge for any non-trivial domain comes from diverse sources and at multiple points in time. Multiple sources need to interoperate, to work together, at both a syntactic level and a semantic level.

    Recall (page 67) that an ontology is a specification of the meanings of the symbols in an information system. Here an information system can be a knowledge base, a sensor such as a thermometer, or some other source of information. The meaning of the symbols is sometimes just in the mind of the knowledge base designer, in a user manual, or in comments with the knowledge base. Increasingly, the specification of the meaning is in machine-interpretable form. This formal specification of the meaning is important for semantic interoperability – the ability of different knowledge bases to work together at a semantic level so that the meanings of symbols are respected.

    Example 14.1 A purchasing agent has to know, when a website claims it has a good price on “chips,” whether these are potato chips, computer chips, wood chips, or poker chips.

  • 8 - Reasoning with Uncertainty

  • from Part III - Reasoning, Learning and Acting with Uncertainty
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 343-424

  • Summary

    It is remarkable that a science which began with the consideration of games of chance should become the most important object of human knowledge … The most important questions of life are, for the most part, really only problems of probability …

    The theory of probabilities is at bottom nothing but common sense reduced to calculus.

    – Pierre Simon de Laplace [1812]

    Agents in real environments are inevitably forced to make decisions based on incomplete information. Even when an agent senses the world to find out more information, it rarely finds out the exact state of the world. For example, a doctor does not know exactly what is going on inside a patient, a teacher does not know exactly what a student understands, and a robot does not know what is in a room it left a few minutes ago. When an intelligent agent must act, it has to use whatever information it has. This chapter considers reasoning with uncertainty that arises whenever an agent is not omniscient. This is used in Chapter 9 as a basis for acting with uncertainty. This chapter starts with probability, shows how to represent the world by making appropriate independence assumptions, and shows how to reason with such representations.

    Probability

    To make a good decision, an agent cannot simply assume what the world is like and act according to that assumption. It must consider multiple hypotheses when making a decision. Consider the following example.

    Example 8.1 Many people consider it sensible to wear a seat belt when traveling in a car because, in an accident, wearing a seat belt reduces the risk of serious injury. However, consider an agent that commits to assumptions and bases its decision on these assumptions. If the agent assumes it will not have an accident, it will not bother with the inconvenience of wearing a seat belt. If it assumes it will have an accident, it will not go out. In neither case would it wear a seat belt! A more intelligent agent may wear a seat belt because the inconvenience of wearing a seat belt is far outweighed by the increased risk of injury or death if it has an accident.

  • The pros and cons of listening devices

  • ROBERT DALE
  • Journal:
    Natural Language Engineering / Volume 23 / Issue 6 / November 2017
    Published online by Cambridge University Press:
    25 September 2017, pp. 969-973
    • Article
      • You have access
      • Open access
    • PDF
    • HTML
    • Export citation

  • Vastly improved speech recognition, backed by a more slowly improving ability to make sense of the recognized speech, has brought state-of-the-art NLP into our homes in the form of smart speakers and other devices that listen. There’s no doubt these devices can be incredibly useful, but they also may also support incursions into our privacy. We look at where we are today, consider what might be coming, and express just a tad of caution.

  • 5 - Propositions and Inference

  • from Part II - Reasoning, Planning and Learning with Certainty
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 173-238

  • Summary

    For when I am presented with a false theorem, I do not need to examine or even to know the demonstration, since I shall discover its falsity a posteriori by means of an easy experiment, that is, by a calculation, costing no more than paper and ink, which will show the error no matter how small it is …

    And if someone would doubt my results, I should say to him: “Let us calculate, Sir,” and thus by taking to pen and ink, we should soon settle the question.

    – GottfriedWilhelm Leibniz [1677]

    This chapter considers a simple form of a knowledge base that is told what facts and rules hold in the world. An agent can use such a knowledge base, together with its observations, to determine what else must be true in the world. When queried about what must be true given a knowledge base, it answers the query without enumerating the possible worlds, or even generating any possible worlds. This chapter presents a number of reasoning formalisms that use propositions. They differ in what is being proved, what background knowledge must be provided, and how observations are handled.

    Propositions

    Statements about the world provide constraints about what could be true. Constraints could be specified extensionally (page 130) as tables of legal assignments to variables, or intensionally (page 130) in terms of formulas. For many domains propositions provide a suitable language for providing intensional constraints.

    There are a number of reasons for using propositions for specifying constraints and queries:

  • • It is often more concise and readable to give a logical statement about the relationship among some variables than to use an extensional representation.

  • • The form of the knowledge can be exploited to make reasoning more efficient.

  • • They are modular, so small changes to the problem result in small changes to the knowledge base. This also means that a knowledge base is easier to debug than for other representations.

  • • The kind of queries an agent may have to answer may be richer than single assignments of values to variables.

  • • This language is extended to reason about individuals and relations in Chapter 13.

  • 1 - Artificial Intelligence and Agents

  • from Part I - Agents in the World: What are Agents and How Can They be Built?
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 3-48

  • Summary

    The history of AI is a history of fantasies, possibilities, demonstrations, and promise. Ever since Homer wrote of mechanical “tripods” waiting on the gods at dinner, imagined mechanical assistants have been a part of our culture. However, only in the last half century have we, the AI community, been able to build experimental machines that test hypotheses about the mechanisms of thought and intelligent behavior and thereby demonstrate mechanisms that formerly existed only as theoretical possibilities.

    – Bruce Buchanan [2005]

    This book is about artificial intelligence, a field built on centuries of thought, which has been a recognized discipline for over 60 years. As Buchanan points out in the quote above, we now have the tools to test hypotheses about the nature of thought itself, as well as to solve practical tasks. Deep scientific and engineering problems have already been solved and many more are waiting to be solved. Many practical applications are currently deployed and the potential exists for an almost unlimited number of future applications. In this book, we present the principles that underlie intelligent computational agents. These principles can help you understand current and future work in AI and equip you to contribute to the discipline yourself.

    What is Artificial Intelligence?

    Artificial intelligence, or AI, is the field that studies the synthesis and analysis of computational agents that act intelligently. Let us examine each part of this definition.

    An agent is something that acts in an environment; it does something. Agents include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries.

    We are interested in what an agent does; that is, how it acts. We judge an agent by its actions.

    An agent acts intelligently when

  • • what it does is appropriate for its circumstances and its goals, taking into account the short-term and long-term consequences of its actions

  • • it is flexible to changing environments and changing goals

  • • it learns from experience

  • • it makes appropriate choices given its perceptual and computational limitations

    • A computational agent is an agent whose decisions about its actions can be explained in terms of computation. That is, the decision can be broken down into primitive operations that can be implemented in a physical device.

  • 15 - Relational Planning, Learning, and Probabilistic Reasoning

  • from Part IV - Reasoning, Learning and Acting with Individuals and Relations
  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp 691-728

  • Summary

    What is now required is to give the greatest possible development to mathematical logic, to allow to the full the importance of relations, and then to found upon this secure basis a new philosophical logic, which may hope to borrow some of the exactitude and certainty of its mathematical foundation. If this can be successfully accomplished, there is every reason to hope that the near future will be as great an epoch in pure philosophy as the immediate past has been in the principles of mathematics. Great triumphs inspire great hopes; and pure thought may achieve, within our generation, such results as will place our time, in this respect, on a level with the greatest age of Greece.

    – Bertrand Russell [1917]

    The representation dimension (page 23) has, as its top level, reasoning in terms of individuals and relations. Reasoning in terms of relations allows for compact representations that can be built before the agent encounters particular individuals. When an agent finds out about an individual, it can make inferences about that individual. This chapter outlines how, in planning, learning and probabilistic reasoning, feature-based representations can be expanded to deal also with individuals and relations. In each of these areas, the relational representation benefits from being able to be built before the individuals are known and, therefore, before the features are known. As Russell points out in the quote above, relational reasoning brings great advantages over propositional and feature-based representations.

    Planning with Individuals and Relations

    A robot that can deliver parcels to people needs a model of the world before it knows which parcels exist and which people may need deliveries. It might need to be programmed before it knows the environment which it will inhabit. A tutoring system needs to work for multiple students and multiple problems, and needs to be programmed before it knows about the students and all of the problems. A purchasing agent, when it is being designed and built, will not know about all of the hotels and rooms it can book, and will not know about the people and their goals or preferences. In all of these cases, the agent's goals and its environment are described in terms of individuals and relations.

  • Preface

  • David L. Poole, University of British Columbia, Vancouver, Alan K. Mackworth, University of British Columbia, Vancouver
  • Book:
    Artificial Intelligence
    Published online:
    12 August 2019
    Print publication:
    25 September 2017, pp xxiii-xxviii

  • Summary

    Artificial Intelligence: Foundations of Computational Agents is a book about the science of artificial intelligence (AI). AI is the study of the design of intelligent computational agents. The book is structured as a textbook but it is designed to be accessible to a wide audience.

    We wrote this book because we are excited about the emergence of AI as an integrated science. As with any science being developed, AI has a coherent, formal theory and a rambunctious experimental wing. Here we balance theory and experiment and show how to link them together intimately. We develop the science of AI together with its engineering applications. We believe the adage, “There is nothing so practical as a good theory.” The spirit of our approach is captured by the dictum, “Everything should be made as simple as possible, but not simpler.” We must build the science on solid foundations; we present the foundations, but only sketch, and give some examples of, the complexity required to build useful intelligent systems. Although the resulting systems will be complex, the foundations and the building blocks should be simple.

    This second edition results from extensive revision throughout the text. We have restructured the material based on feedback from instructors who have used the book in classes. We have brought it up to date to reflect the current state of the art, made parts that were difficult for students more straightforward, added more intuitive explanations, and coordinated the pseudocode algorithms with new open-source implementations of the algorithms in Python and Prolog. We have resisted the temptation to just keep adding more material. AI research is expanding so rapidly now that the volume of potential new text material is vast. However, research teaches us not only what works but also what does not work so well, allowing us to be highly selective. We have included more material on machine learning techniques that have proven successful. However, research also has trends and fashions. We have removed techniques that have been shown to be less promising, but we distinguish them from the techniques that are merely out of fashion. We include some currently unfashionable material if the problems attacked still remain and the techniques have the potential to form the basis for future research and development.

Page 56 of 167