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1 Introduction

Many important problems involve decision making under uncertainty, including

aircraft collision avoidance, wildfire management, and disaster response. When

designing automated decision-making systems or decision-support systems, it is

important to account for the various sources of uncertainty when making or recommending decisions. Accounting for these sources of uncertainty and carefully

balancing the multiple objectives of the system can be very challenging. We will

discuss these challenges from a computational perspective, aiming to provide

the theory behind decision-making models and computational approaches. This

chapter introduces the problem of decision making under uncertainty, provides

some example applications, and outlines the space of possible computational

approaches. The chapter then summarizes how various disciplines have contributed to our understanding of intelligent decision making and highlights areas

of potential societal impact. We conclude with an outline of the remainder of the

book.

1.1 Decision Making

An agent is something that acts based on observations of its environment. Agents

may be physical entities, like humans or robots, or they may be nonphysical entities, such as decision support systems that are implemented entirely in software.

As shown in figure 1.1, the interaction between the agent and the world follows

an observe-act cycle or loop.

The agent at time treceives an observation of the world, denoted ot

. Observations

may be made, for example, through a biological sensory process as in humans

or by a sensor system like radar in an air traffic control system. Observations are

often incomplete or noisy; humans may not see an approaching aircraft or a radar