Artificial intelligence is a transformational set of ideas, algorithms, and tools. AI systems are now increasingly deployed at scale in the real world [Littman et al., 2021; Zhang et al., 2022a]. They have significant impact across almost all forms of human activity, including the economic, social, psychological, healthcare, legal, political, government, scientific, technological, manufacturing, military, media, educational, artistic, transportation, agricultural, environmental, and philosophical spheres.

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 flexible methods for storing and reasoning with facts, and knowledge and data sharing using ontologies. As Smith points out, the problems of ontology are central for building intelligent computational agents.

A reinforcement learning (RL) agent acts in an environment, observing its state and receiving rewards. From its experience of a stream of acting then observing the resulting state and reward, it must determine what to do given its goal of maximizing accumulated reward. This chapter considers fully observable (page 29), single-agent reinforcement learning.

This chapter considers simple forms of reasoning in terms of propositions – statements that can be true or false. Some reasoning includes model finding, finding logical consequences, and various forms of hypothetical reasoning. Semantics forms the foundations of specification of facts, reasoning, and debugging.

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.

Deterministic planning is the process of finding a sequence of actions to achieve a goal. Because an agent does not usually achieve its goals in one step, what it should do at any time depends on what it will do in the future. What it will do in the future depends on the state it is in, which, in turn, depends on what it has done in the past. This chapter presents representations of actions and their effects, and some offline algorithms for an agent to find a plan to achieve its goals from a given state.

Temporal prepositions trigger various temporal relations over events and times. In this chapter, I categorize such temporal relators into five types: (i) anchoring (at, in), (ii) ordering (before, after),(iii) metric (for, in), (iv) bounding (from -- till), and (v) orienting (time interval + before, after). These temporal relators are analyzed with respect to the tripartite temporal configurations <E,R,T>, where E is a set of eventualities, R is a set of temporal relators, and T is a set of associated temporal structures, which subsume metric structures. Temporal relators combine with temporal expressions to form temporal adjuncts, either simple or complex. Complex temporal adjuncts introduce time intervals as nonconsuming tag in annotation, while relating eventualities to temporal structures. Each temporal relator r in R combines with a temporal structure t in T as its argument to form a temporal adjunct, while relating an eventuality e in E of various aspectual types such as state, process, or transition to an appropriate temporal structure t in T. This chapter clarifies such temporal relations by annotating and interpreting event and temporal base structures and their relations.

In this chapter, I explain how TimeML, a specification language for the annotation of event-associated temporal expressions in language, was normalized as an ISO international standard, known as ISO-TimeML, with some modifications. ISO’s working group developed TimeML into an ISO standard on event-associated temporal annotation by making four modifications of TimeML: (i) abstract specification of the annotation scheme, (ii) adoption of standoff annotation, (iii) merging of two tags, <EVENT/> and <MAKEINSTNACE/>, to a single tag <EVENT/>, and (iv) treating duration (e.g., two hours) as measurement. Following Bunt’s (2010) proposal for the construction of semantic annotation schemes and his subsequent work, I then formalize ISO-TimeML by presenting a metamodel for its design, an abstract syntax for formulating its specification language in set-theoretic terms, and an XML-based concrete syntax. I also analyze base structures as consisting of two substructures, anchoring and content structures, into the annotation structures of the normalized TimeML.

This chapter formulates an annotation-based semantics (ABS) for the annotation and interpretation of temporal and spatial information in language. It consists of two modules, one for representation and another for interpretation. The representation module consists of a type-theoretic first-order logic with a small set of merge operators. The theory of types is based on the extended list of basic types, which treats eventualities and points of time and space as basic types besides the two basic types e for entities and t for truth-values. These types extend the Neo-Davidsonian semantics to all types of objects including paths and vectors (trajectories) triggered by motions. The merge operators in ABS allow the compositional process of combining the semantic representation of base structures into that of the link structures that combine them without depending on complex lambda operations. ABS adopts shallow semantics to represent complex structures of eventuality or quantificaiton with simple logical predicates defined as part of admissible interpretation models.