The previous chapters have shown how practical reasoning is used in arguments, but it will turn out to be vitally important to understand how it is used in explanations. It will even be shown at the end of this chapter how the same example of practical reasoning in a discourse can combine explanations with arguments. Hence, there arises the problem of building a model of explanation to reveal precisely how practical reasoning is used in explanations. The key to solving it, as will be shown in this chapter, is to broaden the study of practical reasoning to take into account not only its structure as a chain of reasoning, but also how that same kind of reasoning can be used in different ways in different communicative settings. An argument will be shown to be a response to a particular kind of question, while an explanation will be seen as a response to another kind of question.

Recent work in artificial intelligence has taken the approach that an explanation is best seen as a transfer of understanding from one party to another in a dialogue where one party is a questioner who asks why or how something works and the other party attempts to fulfill this request (Cawsey, 1992; Moore, 1995; Moulin et al., 2002). Recent literature in philosophy of science seems to be gradually moving toward this approach, but there is an open question of how it can be represented using a formal structure (Trout, 2002). Since explanations and arguments are sometimes hard to distinguish, the first step is to provide some way of representing the distinction between them in their formal structure. In this chapter, the Why2 Dialogue System is presented as a formal model showing how the difference between argument and explanation resides in the pre- and post-conditions for the speech act of requesting an argument and the speech act of requesting an explanation. It is an extension of earlier dialogue systems (Walton, 2004, 2007a, 2011).

Practical reasoning of the kind described by philosophers since Aristotle (384–322 BC) is identified as goal-based reasoning that works by finding a sequence of actions that leads toward or reaches an agent's goal. Practical reasoning, as described in this book, is used by an agent to select an action from a set of available alternative actions the agent sees as open in its given circumstances. A practical reasoning agent can be a human or an artificial agent – for example, software, a robot, or an animal. Once the action is selected as the best or most practical means of achieving the goal in the given situation, the agent draws a conclusion that it should go ahead and carry out this action. Such an inference is fallible, as long as the agent's knowledge base is open to new information. It is an important aspect of goal-based practical reasoning that if an agent learns that its circumstances or its goals have changed and a different action might now become the best one available, it can (and perhaps should) “change its mind.”

In computer science, practical reasoning is more likely to be known as means-end reasoning (where an end is taken to mean a goal), goal-based reasoning, or goal-directed reasoning (Russell and Norvig, 1995, 259). Practical reasoning is fundamental to artificial intelligence (Reed and Norman, 2003), where it is called means-end analysis (Simon, 1981). In goal-based problem-solving, a search for a solution to a problem is carried out by finding a sequence of actions from available means of solving a problem. An intelligent goal-seeking agent needs to receive information about its external circumstances by means of sensors, and store it in its memory. There are differences of opinion about how practical goal-based reasoning should be modeled. One issue is whether it should be seen as merely an instrumental form of reasoning, or whether it should be also based on values. Many automated systems of practical reasoning for multi-agent deliberation (Gordon and Richter, 2002; Atkinson et al., 2004a, 2004b; Rahwan and Amgoud, 2006) take values into account.

The most basic problem that led to the other problems studied in the book was posed in Chapter 1. If you try to model the given instance of practical reasoning as a sequence of argumentation only by an argument map, you are led to a state space explosion. Throughout the subsequent chapters we have moved toward a solution to this problem by embedding practical reasoning in an overarching procedural framework in which any given sequence of practical reasoning should be viewed as a part of a deliberation dialogue having an opening stage and a closing stage. This problem led to Chapter 6, where criteria for the proper closure of a deliberation dialogue were proposed. As shown in Chapter 6, practical reasoning is most characteristically used in deliberation dialogue – goal-directed dialogue in which a choice for action needs to be made or problem needs to be solved. It was also shown in Chapter 6 that that deliberation dialogue is often mixed in with information-seeking dialogue as new evidence of the circumstances comes in. Also, as early as Chapter 2, it was shown that practical reasoning is used in persuasion dialogue – for example, in ads for medical products.

Atkinson et al. (2013) showed that there are also many shifts in a deliberation dialogue to persuasion dialogue intervals. Typically, for example, a proposal that has been put forward as part of a deliberation dialogue is attacked by a critic who shifts to a persuasion dialogue in order to attack the arguments that were used to support the proposal that was made in the deliberation dialogue. It is important to see that there is nothing inherently illegitimate about such shifts.

However, a general problem arises from the variability of different communicative multi-agent settings in which practical reasoning is used. As seen in the examples from Chapter 6, deliberation dialogue is the most important and central setting in which practical reasoning is used, and the true colors of practical reasoning as a form of argumentation really begin to emerge once we embed it into this setting. Nevertheless, we also need to confront the underlying problem that in the argumentation in natural language examples where practical reasoning is used, there so often appear to be dialectical shifts from deliberation dialogue to persuasion dialogue.

In Chapter 2 it was shown how there are different frameworks of communication in which arguments can be put forward and critically questioned, including persuasion dialogue, information-seeking dialogue, and deliberation dialogue. This chapter will focus almost exclusively on deliberation dialogue, but will also deal with related issues where there is a shift to or from one of these other types of dialogue to deliberation dialogue. It will be shown how practical reasoning is woven through every aspect of deliberation dialogue, and how deliberation dialogue represents the necessary framework for analyzing and evaluating typical instances of practical reasoning in natural language cases of argumentation that we are all familiar with. This chapter will also show how formal models of deliberation dialogue built as artificial intelligence tools for multi-agent systems turn out to be extremely useful for solving the closure problem of practical reasoning in multi-agent settings.

The chapter begins by using four examples to show how practical reasoning is embedded in everyday deliberations of a kind all of us are familiar with. The first one, in Section 1, is a case of a man trying to solve the problem with his printer by looking on Google to get advice and then using a trial and error procedure to try to fix the problem. The second one, in Section 2, is an example of a couple trying to arrive at a decision on which home to buy, having narrowed the choices down to three candidates: a condominium, a two-story house, and a bungalow. The third one, in Section 3, is a case of a policy debate, showing how CAS employs practical reasoning in this setting. The fourth (Section 4), is a town hall meeting on a decision of whether or not to bring in no-fault insurance. Section 5 explains the essentials of the leading model of deliberation dialogue used in artificial intelligence at this point – the McBurney, Hitchcock, and Parsons (MHP) model.

Ascription of an intention to an agent is especially important in law. In criminal law the intent to commit a criminal act, called mens rea, refers to the guilty mind, the key element needed to prosecute a defendant for a crime. For example, in order to prove that a defendant has committed the crime of theft of an object, it needs to be established that the defendant had the intention never to return the object to its owner. Studying examples of how intention is proved in law is an important resource for giving us clues on how reasoning to an intention should be carried out. Intention is also fundamentally important in ethical reasoning where there are problems about how the end can justify the means.

This chapter introduces the notion of inference to the best explanation, often called abductive reasoning, and presents recent research on evidential reasoning that uses the concept of a so-called script or story as a central component. The introduction of these two argumentation tools show how they are helpful in moving forward toward a solution to the longstanding problem of analyzing how practical reasoning from circumstantial evidence can be used to support or undermine a hypothesis that an agent has a particular intention. Legal examples are used to show that even though ascribing an intention to an agent is an evaluation procedure that combines argumentation and explanation, it can be rationally carried out by using a practical reasoning model that accounts for the weighing of factual evidence on both sides of a disputed case.

The examples studied in this chapter will involve cases where practical reasoning is used as the glue that combines argumentation with explanation. Section 1 considers a simple example of a message on the Internet advising how to mount a flagpole bracket to a house. The example tells the reader how to take the required steps to attach a bracket to the house in order to mount a flagpole so that the reader can show his patriotism by displaying a flag on his house. The example text is clearly an instance of practical reasoning. The author of the ad presumes that the reader has a goal, and he tells the reader how to fulfill that goal by carrying out a sequence of actions.