Trial and error in the context of "Domestication"

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.

Informal learning is characterized "by a low degree of planning and organizing in terms of the learning context, learning support, learning time, and learning objectives". It differs from formal learning, non-formal learning, and self-regulated learning, because it has no set objective in terms of learning outcomes, but an intent to act from the learner's standpoint (e.g., to solve a problem). Typical mechanisms of informal learning include trial and error or learning-by-doing, modeling, feedback, and reflection. For learners this includes heuristic language building, socialization, enculturation, and play. Informal learning is a pervasive ongoing phenomenon of learning via participation or learning via knowledge creation, in contrast with the traditional view of teacher-centered learning via knowledge acquisition. Estimates suggest that about 70-90 percent of adult learning takes place informally and outside educational institutions.

The term is often conflated, however, with non-formal learning, and self-directed learning. It is widely used in the context of corporate training and education in relation to return on investment (ROI), or return on learning (ROL). It is also widely used when referring to science education, in relation to citizen science, or informal science education. The conflated meaning of informal and non-formal learning explicates mechanisms of learning that organically occur outside the realm of traditional instructor-led programs, e.g., reading self-selected books, participating in self-study programs, navigating performance support materials and systems, incidental skills practice, receptivity of coaching or mentoring, seeking advice from peers, or participation in communities of practice, to name a few. Informal learning is common in communities where individuals have opportunities to observe and participate in social activities. Advantages of informal learning cited include flexibility and adaptation to learning needs, direct transfer of learning into practice, and rapid resolution of (work-related) problems. For improving employees' performance, task execution is considered the most important source of learning.

In probability theory, the sample space (also called sample description space, possibility space, or outcome space) of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, are listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters, or symbols. They can also be finite, countably infinite, or uncountably infinite.

A subset of the sample space is an event, denoted by ${\displaystyle E}$. If the outcome of an experiment is included in ${\displaystyle E}$, then event ${\displaystyle E}$ has occurred.

Bulls and cows (also known as cows and bulls or pigs and bulls) is a code-breaking mind or paper and pencil game for two or more players. The game is played in turns by two opponents who aim to decipher the other's secret code by trial and error.

Bulls and cows predates the commercially marketed board game version, Mastermind, and its word-based variant predates the gameshow Lingo and web game Wordle. A version known as MOO was widely available for early mainframe computers, Unix and Multics systems, among others.

Cat intelligence refers to a cat’s ability to solve problems, adapt to its environment, learn new behaviors, and communicate its needs. Structurally, a cat’s brain shares similarities with the human brain, containing around 250 million neurons in the cerebral cortex, which is responsible for complex processing. Cats display neuroplasticity, allowing their brains to reorganize based on experiences. They have well-developed memory retaining information for a decade or longer. These memories are often intertwined with emotions, allowing cats to recall both positive and negative experiences associated with specific places. While they excel in observational learning and problem-solving, studies conclude that they struggle with understanding cause-and-effect relationships in the same way that humans do.

The study of cat intelligence is mostly focused on domesticated cats. Living in urban environments has exposed them to challenges that require adaptive behaviors, contributing to cognitive development. Selective breeding and genetic changes have further influenced their intelligence. Kittens learn essential survival skills by observing their mothers, while adult cats refine their abilities through trial and error.

In mathematics, the regula falsi, method of false position, or false position method refers to a family of algorithms used to solve linear equations and smooth nonlinear equations for a single unknown value.In its oldest known examples found in cuneiform and hieroglyphic writings, the method replaces simple trial and error with proportional correction of an initial guess. In modern usage, the method relies on linear interpolation based on two different guesses.