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.

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 goals to determine what to do.

Once the Court has established that an interference with a Convention right meets the requirement of lawfulness, the second main requirement for the interference to be justifiable is that it pursues a legitimate aim. The express limitation clauses of the Convention further do not only require the aims pursued to be legitimate, but they also provide exhaustive lists of the aims which can legitimately be served. Although the test of legitimate aim does not play a large role in the Court’s review of justification for restrictions, this Chapter addresses the application and interpretation of the legitimate aim requirement. Special attention is paid to the situation of a discrepancy between stated and real aims and the situation where more than one aim is pursued. In addition, Article 18 ECHR is discussed, which states that restrictions cannot be applied for any other purpose than that for which they have been prescribed. The Merabishvili case and the requirements defined therein are central to this discussion.

An agent that is not omniscient cannot just plan a fixed sequence of steps, as was assumed in Chapter 6. Planning must take into account the fact that an agent in the real world does not know what will actually happen when it acts, nor what it will observe in the future. An agent should plan to react to its environment.

This chapter shows how an intelligent agent can perceive, reason, and act over time in an environment. In particular, it considers the internal structure of an agent. As Simon points out in the quote above, hierarchical decomposition is an important part of the design of complex systems such as intelligent agents.

The previous chapter assumed that the input were features; you might wonder where the features come from. The inputs to real-world agents are diverse, including pixels from cameras, sound waves from microphones, or character sequences from web requests. Using these directly as inputs to the methods from the previous chapter often does not work well; useful features need to be created from the raw inputs.

In the example from Pearl (above), mud and rain are correlated, but the relationship between mud and rain is not symmetric. Creating mud (e.g., by pouring water on dirt) does not make rain. However, if you were to cause rain (e.g., by seeding clouds), mud will result. There is a causal relationship between mud and rain: rain causes mud, and mud does not cause rain.

In Stage 3 of its review the Court has to examine if the contested interference can be held to be justified by objective and convincing reasons. One of the requirements for a restriction to be justifiable is the lawfulness requirement. The requirement has been given an autonomous and substantive reading by the Court, which entails that a restriction must have a basis in domestic law, the legal basis must be sufficiently accessible, the interference must be sufficiently foreseeable, and it must not be arbitrary. In addition, in particular in cases regarding surveillance, searches and the exercise of other discretionary powers, the Court has required that procedural safeguards must be offered. All these requirements are discussed in this chapter.

What should an agent do when there are other agents, with their own goals and preferences, who are also reasoning about what to do? An intelligent agent should not ignore other agents or treat them as noise in the environment. This chapter considers the problems of determining what an agent should do in an environment that includes other agents who have their own utilities.

This chapter is about how to represent individuals (things, entities, objects) and relationships among them. As Baum suggests in the quote above, the real world contains objects and compact representations of those objects and relationships can make reasoning about them tractable. Such representations can be much more compact than representations in terms of features alone.

In the machine learning and probabilistic models presented in earlier chapters, the world is made up of features and random variables. As Pinker points out, we generally reason about things. Things are not features or random variables; it doesn’t make sense to talk about the probability of an individual animal, but you could reason about the probability that it is sick, based on its symptoms.

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, where a feature is a function on states. 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.