The main goal of this chapter is to prepare some terminology to be used in the rest of the book. One important notion introduced in this chapter is that of the undropping game. We will use it to prove a comparison theorem for hod mice in Chapter 4.

This chapter covers:

1. • The difference between affect, emotions, and mood;

2. • What roles emotions play in interacting with other humans and robots;

3. • Basic models of emotions;

4. • The challenges in emotion processing.

This chapter covers:

1. • The basic hardware and software components that a robot consists of;

2. • The techniques we can apply to make a robot ready for interacting with people.

This chapter covers:

1. • Current attitude of the general public toward robots and how this may change in the coming years;

2. • Possible shifts and developments in the nature of human–robot relationships, specifically companion bots;

3. • Further development of the technology of human–robot interactions, specifically artificial intelligence;

4. • The inherent issues with predicting the future (“crystal ball problems”).

This chapter studies internal theory of lsa hod mice. Suppose $(\mathcal{P},\Sigma)$ is a hod pair of an sts hod pair, $X$ is a self-wellordered set such that $\mathcal{P}\in X$, and $\mathcal{N}$ is a $\Sigma$ or $\Sigma$-sts mouse over $X$. The main theorem of this chapter shows that N is $\Sigma$-closed and has fullness preserving iteration strategy, then $\Sigma \restriction \mathcal{N}[g]$ is definable in $\mathcal{N}[g]$ for any generic $g$ over $\mathcal{N}$ . The main idea behind the proof is that the branch of an iteration tree $\mathcal{T}$ on $\mathcal{P}$ can be identified by the authentication process introduced in Chapter 3.

This chapter covers:

1. • The complexities and challenges of human verbal interaction;

2. • The components of speech in human and human–robot interaction (HRI);

3. • The basic principles of speech recognition and its application to HRI;

4. • Dialogue management systems in HRI;

5. • Natural-language interaction in HRI, including the use of chatbots.

This chapter covers:

1. • The importance of the spatial placement of agents in social interaction;

2. • Basic human proxemics: how people manage space in relation to others;

3. • How a robot manages the space around it, including interactions such as approaching, initiating interaction, maintaining distance, and navigating around people;

4. • How the properties of spatial interaction can be used as cues for robots.

This chapter gives a proof of generic interpretability for (pre)hod pairs, studies derived models of hod mice, and proves branch condensation holds on a tail for anomalous hod pairs of type II and III.

This chapter presents a construction of the minimal model of LSA from a hypothesis implied by strong forcing axioms such as PFA and by large cardinal hypotheses such as the existence of a strongly compact cardinal. Consequently, LSA is consistent relative to PFA and LSA is consistent relative to the existence of a strongly compact cardinal. This chapter is an application of the theory developed in the previous chapters and the core model induction technique, which is a general method for calibrating consistency strength of strong theories.

This chapter covers:

1. • Methodological considerations and various decisions you need to make in setting up and performing a human–robot interaction (HRI) study;

2. • The strengths and weaknesses of different research methods and how to identify them for understanding and evaluating HRI;

3. • How the choice of robot, environment, and context matter for study results;

4. • The importance of looking at new ways of reporting data and insights befitting HRI, even though there is a tradition of reporting experimental work.

This chapter covers:

1. • The influence of the media on human–robot interaction (HRI) research;

2. • Stereotypes of robots in the media;

3. • Positive and negative visions of HRI;

4. • Ethical considerations when designing an HRI study;

5. • Ethical issues of robots that fulfill a user’s emotional needs;

6. • The dilemmas associated with behavior toward robots (e.g., robots’ right to be treated in a moral way);

7. • The issue of job losses as a result of the increasing number of robots in the workforce.

This chapter covers:

1. • What different social science theories say about how people form perceptions about others;

2. • How we understand anthropomorphism of robots based on prior social science literature;

3. • How anthropomorphism makes us see robots as uncanny, trustworthy, or likable.

This chapter introduces recursive difference equations where the initial conditions are nonzero. The output of such a system is studied in detail. One application is in the design of digital waveform generators such as oscillators, and this is explained in considerable detail. The coupled-form oscillator, which simultaneously generates synchronized sine and cosine waveforms at a given frequency, is presented. The chapter also introduces another application of recursive difference equations, namely the computation of mortgages. It is shown that the monthly payment on a loan can be computed using a first-order recursive difference equation. The equation also allows one to calculate the interest and principal parts of the payment every month, as shown. Poles play a crucial role in the behavior of recursive difference equations with zero or nonzero initial conditions. Many different manifestations of the effect of a pole are also summarized, including some time-domain dynamical meanings of poles.

Networks can get big. Really big. Examples include web crawls, online social networks, and knowledge graphs. Networks from these domains can have billions of nodes and hundreds of billions of edges. Systems biology is yet another area where networks will continue to grow. As sequencing methods continue to advance, more networks and larger, denser networks will need to be analyzed. This chapter discusses some of the challenges you face and solutions you can try when scaling up to massive networks. These range from implementation details to new algorithms and strategies to reduce the burden of such big data. Various tools, such as graph databases, probabilistic data structures, and local algorithms, are at our disposal, especially if we can accept sampling effects and uncertainty.