Artificial intelligence in the context of Robot control

Argumentation theory is the interdisciplinary study of how conclusions can be supported or undermined by premises through logical reasoning. With historical origins in logic, dialectic, and rhetoric, argumentation theory includes the arts and sciences of civil debate, dialogue, conversation, and persuasion. It studies rules of inference, logic, and procedural rules in both artificial and real-world settings.

Argumentation includes various forms of dialogue such as deliberation and negotiation which are concerned with collaborative decision-making procedures. It also encompasses eristic dialogue, the branch of social debate in which victory over an opponent is the primary goal, and didactic dialogue used for teaching. This discipline also studies the means by which people can express and rationally resolve or at least manage their disagreements.

Computer science is the study of computation, information, and automation. Included broadly in the sciences, computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). An expert in the field is known as a computer scientist.

Algorithms and data structures are central to computer science.The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

Natural language processing (NLP) is the processing of natural language information by a computer. The study of NLP, a subfield of computer science, is generally associated with artificial intelligence. NLP is related to information retrieval, knowledge representation, computational linguistics, and more broadly with linguistics.

Major processing tasks in an NLP system include: speech recognition, text classification, natural language understanding, and natural language generation.

Behavior (American English) or behaviour (British English) is the range of actions of individuals, organisms, systems or artificial entities in some environment. These systems can include other systems or organisms as well as the inanimate physical environment. It is the computed response of the system or organism to various stimuli or inputs, whether internal or external, conscious or subconscious, overt or covert, and voluntary or involuntary. While some behavior is produced in response to an organism's environment (extrinsic motivation), behavior can also be the product of intrinsic motivation, also referred to as "agency" or "free will".

Taking a behavior informatics perspective, a behavior consists of actor, operation, interactions, and their properties. This can be represented as a behavior vector.

Formal science is a branch of science studying disciplines concerned with abstract structures described by formal systems, such as logic, mathematics, statistics, theoretical computer science, artificial intelligence, information theory, game theory, systems theory, decision theory and theoretical linguistics. Whereas the natural sciences and social sciences seek to characterize physical systems and social systems, respectively, using theoretical and empirical methods, the formal sciences use language tools concerned with characterizing abstract structures described by formal systems and the deductions that can be made from them. The formal sciences aid the natural and social sciences by providing information about the structures used to describe the physical world, and what inferences may be made about them.

Inferences are steps in logical reasoning, moving from premises to logical consequences; etymologically, the word infer means to "carry forward". Inference is theoretically traditionally divided into deduction and induction, a distinction that in Europe dates at least to Aristotle (300s BC). Deduction is inference deriving logical conclusions from premises known or assumed to be true, with the laws of valid inference being studied in logic. Induction is inference from particular evidence to a universal conclusion. A third type of inference is sometimes distinguished, notably by Charles Sanders Peirce, contradistinguishing abduction from induction.

Various fields study how inference is done in practice. Human inference (i.e. how humans draw conclusions) is traditionally studied within the fields of logic, argumentation studies, and cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference. Statistical inference uses mathematics to draw conclusions in the presence of uncertainty. This generalizes deterministic reasoning, with the absence of uncertainty as a special case. Statistical inference uses quantitative or qualitative (categorical) data which may be subject to random variations.

Johor is a state of Malaysia in the south of the Malay Peninsula. It borders with Pahang, Malacca and Negeri Sembilan to the north. Johor has maritime borders with Singapore to the south and Indonesia to the east and west. As of 2023, the state's population is 4.09 million, making it the second most populous state in Malaysia, after Selangor. Johor Bahru is the capital city and the economic centre of the state, Kota Iskandar is the state administrative centre and Muar serves as the royal capital.

As one of the nation's most important economic hubs, Johor has the highest gross domestic product (GDP) in Malaysia outside of the Klang Valley, making it the country's second largest state economy, behind Selangor. Its household income and total salaries are the second highest among all Malaysian states. Johor has the world's second largest artificial intelligence hub, robust manufacturing and logistics centres, and home to the Port of Tanjung Pelepas, the 15th busiest port in the world. Iskandar Malaysia, which covers much of southern Johor, is the country's largest special economic zone by investment value.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance.

ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics.

Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition (in a broad sense). Mental faculties of concern to cognitive scientists include perception, memory, attention, reasoning, language, and emotion. To understand these faculties, cognitive scientists borrow from fields such as psychology, philosophy, artificial intelligence, neuroscience, linguistics, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision-making to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

A vocabulary (also known as a lexicon) is a set of words, typically the set in a language or the set known to an individual. The word vocabulary originated from the Latin vocabulum, meaning "a word, name". It forms an essential component of language and communication, helping convey thoughts, ideas, emotions, and information. Vocabulary can be oral, written, or signed and can be categorized into two main types: active vocabulary (words one uses regularly) and passive vocabulary (words one recognizes but does not use often). An individual's vocabulary continually evolves through various methods, including direct instruction, independent reading, and natural language exposure, but it can also shrink due to forgetting, trauma, or disease. Furthermore, vocabulary is a significant focus of study across various disciplines, like linguistics, education, psychology, and artificial intelligence. Vocabulary is not limited to single words; it also encompasses multi-word units known as collocations, idioms, and other types of phraseology. Acquiring an adequate vocabulary is one of the largest challenges in learning a second language.

Interactive media refers to digital experiences that dynamically respond to user input, delivering content such as text, images, animations, video, audio, and even AI-driven interactions. Over the years, interactive media has expanded across gaming, education, social platforms, and immersive technologies like VR and AR. With the rise of AI-generated content, decision-driven narratives, and real-time engagement, concerns have shifted toward cybersecurity risks, digital well-being, and the societal impact of hyper-personalized media.

Insight is the understanding of a specific cause and effect within a particular context. The term insight can have several related meanings:

• a piece of information
• the act or result of understanding the inner nature of things or of seeing intuitively (called noesis in Greek)
• an introspection
• the power of acute observation and deduction, discernment, and perception, called intellection or noesis
• an understanding of cause and effect based on the identification of relationships and behaviors within a model, system, context, or scenario (see artificial intelligence)

An insight that manifests itself suddenly, such as understanding how to solve a difficult problem, is sometimes called by the German word Aha-Erlebnis. The term was coined by the German psychologist and theoretical linguist Karl Bühler. It is also known as an epiphany, eureka moment, or (for crossword solvers) the penny dropping moment (PDM). Sudden sickening realisations often identify a problem rather than solving it, so Uh-oh rather than Aha moments are seen in negative insight. A further example of negative insight is chagrin which is annoyance at the obviousness of a solution that was missed up until the (perhaps too late) point of insight, an example of this being Homer Simpson's catchphrase exclamation, D'oh!.

Probability is a branch of mathematics and statistics concerning events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur. This number is often expressed as a percentage (%), ranging from 0% to 100%. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written as 0.5 or 50%).

These concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems.

A mental event is any event that happens within the mind of a conscious individual. Examples include thoughts, feelings, decisions, dreams, and realizations. These events often make up the conscious life that are associated with cognitive function.

Some believe that mental events are not limited to human thought but can be associated with animals and artificial intelligence as well. Whether mental events are identical to complex physical events, or whether such an identity even makes sense, is central to the mind–body problem.

Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it prescribes a vocabulary to articulate and recast the pattern concepts in precise language. Broad in its mathematical coverage, Pattern Theory spans algebra and statistics, as well as local topological and global entropic properties.

In addition to the new algebraic vocabulary, its statistical approach is novel in its aim to:

Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of reasoning.

Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom systems, frames, rules, logic programs, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, model generators, and classifiers.

Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory.

In known environments with available models, planning can be done offline. Solutions can be found and evaluated prior to execution. In dynamically unknown environments, the strategy often needs to be revised online. Models and policies must be adapted. Solutions usually resort to iterative trial and error processes commonly seen in artificial intelligence. These include dynamic programming, reinforcement learning and combinatorial optimization. Languages used to describe planning and scheduling are often called action languages.