The monkey and banana problem is a famous toy problem in artificial intelligence, particularly in logic programming and planning.
The monkey and banana problem is a famous toy problem in artificial intelligence, particularly in logic programming and planning.
A monkey is in a room. Suspended from the ceiling is a bunch of bananas, beyond the monkey's reach. However, in the room there are also a chair and a stick. The ceiling is just the right height so that a monkey standing on a chair could knock the bananas down with the stick. The monkey knows how to move around, carry other things around, reach for the bananas, and wave a stick in the air. What is the best sequence of actions for the monkey?
The problem seeks to answer the question of whether monkeys are intelligent. Both humans and monkeys have the ability to use mental maps to remember things like where to go to find shelter, or how to avoid danger. They can also remember where to go to gather food and water, as well as how to communicate with each other. Monkeys have the ability not only to remember how to hunt and gather but to learn new things, as is the case with the monkey and the bananas: despite the fact that the monkey may never have been in an identical situation, with the same artifacts at hand, a monkey is capable of concluding that it needs to make a ladder, position it below the bananas, and climb up to reach for them.
The degree to which such abilities should be ascribed to instinct or learning is a matter of debate.
In 1984, a pigeon was observed as having the capacity to solve a problem. [1] [2]
The problem is used as a toy problem for computer science. It can be solved with an expert system such as CLIPS. The example set of rules that CLIPS provides is somewhat fragile in that naive changes to the rulebase that might seem to a human of average intelligence to make common sense can cause the engine to fail to get the monkey to reach the banana. [3]
Other examples exist using Rules Based System (RBS) a project implemented in Python. [4] [5]
In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems, assuming intelligence is computational, is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.
The Age of Spiritual Machines: When Computers Exceed Human Intelligence is a non-fiction book by inventor and futurist Ray Kurzweil about artificial intelligence and the future course of humanity. First published in hardcover on January 1, 1999, by Viking, it has received attention from The New York Times, The New York Review of Books and The Atlantic. In the book Kurzweil outlines his vision for how technology will progress during the 21st century.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
Animal cognition encompasses the mental capacities of non-human animals including insect cognition. The study of animal conditioning and learning used in this field was developed from comparative psychology. It has also been strongly influenced by research in ethology, behavioral ecology, and evolutionary psychology; the alternative name cognitive ethology is sometimes used. Many behaviors associated with the term animal intelligence are also subsumed within animal cognition.
Intelligence has been defined in many ways: the capacity for abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. It can be described as the ability to perceive or infer information; and to retain it as knowledge to be applied to adaptive behaviors within an environment or context.
An aptitude is a component of a competence to do a certain kind of work at a certain level. Outstanding aptitude can be considered "talent". Aptitude is inborn potential to perform certain kinds of activities, whether physical or mental, and whether developed or undeveloped. Aptitude is often contrasted with skills and abilities, which are developed through learning. The mass term ability refers to components of competence acquired through a combination of both aptitude and skills.
Sultan, one of the brightest of the early chimpanzees used for psychological research, was tested by Gestalt psychologist Wolfgang Köhler. Sultan is particularly recognized for his insight in solving numerous problems, including stacking or manipulating boxes to reach a reward and use of two sticks as a unit to rake food to a reachable distance.
The Triarchic Theory of Intelligence or Three Forms of Intelligence, formulated by psychologist Robert Sternberg, aims to go against the psychometric approach to intelligence and take a more cognitive approach, which leaves it to the category of the cognitive-contextual theories. The three meta components are also called triarchic components.
In scientific disciplines, a toy problem or a puzzlelike problem is a problem that is not of immediate scientific interest, yet is used as an expository device to illustrate a trait that may be shared by other, more complicated, instances of the problem, or as a way to explain a particular, more general, problem solving technique. A toy problem is useful to test and demonstrate methodologies. Researchers can use toy problems to compare the performance of different algorithms. They are also good for game designing.
Metacognition is an awareness of one's thought processes and an understanding of the patterns behind them. The term comes from the root word meta, meaning "beyond", or "on top of". Metacognition can take many forms, such as reflecting on one's ways of thinking and knowing when and how to use particular strategies for problem-solving. There are generally two components of metacognition: (1) knowledge about cognition and (2) regulation of cognition. A metacognitive model differs from other scientific models in that the creator of the model is per definition also enclosed within it. Scientific models are often prone to distancing the observer from the object or field of study whereas a metacognitive model in general tries to include the observer in the model.
Emotional reasoning is a cognitive process by which an individual concludes that their emotional reaction proves something is true, despite contrary empirical evidence. Emotional reasoning creates an 'emotional truth', which may be in direct conflict with the inverse 'perceptional truth'. It can create feelings of anxiety, fear, and apprehension in existing stressful situations, and as such, is often associated with or triggered by panic disorder or anxiety disorder. For example, even though a spouse has shown only devotion, a person using emotional reasoning might conclude, "I know my spouse is being unfaithful because I feel jealous."
The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. The same name was later used to refer to the formal language of the inputs to this planner. This language is the base for most of the languages for expressing automated planning problem instances in use today; such languages are commonly known as action languages. This article only describes the language, not the planner.
A physical symbol system takes physical patterns (symbols), combining them into structures (expressions) and manipulating them to produce new expressions.
The philosophy of artificial intelligence is a branch of the philosophy of mind and the philosophy of computer science that explores artificial intelligence and its implications for knowledge and understanding of intelligence, ethics, consciousness, epistemology, and free will. Furthermore, the technology is concerned with the creation of artificial animals or artificial people so the discipline is of considerable interest to philosophers. These factors contributed to the emergence of the philosophy of artificial intelligence.
The blocks world is a planning domain in artificial intelligence. The algorithm is similar to a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them.
In computer science, an anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends. The algorithm is expected to find better and better solutions the longer it keeps running.
Moravec's paradox is the observation in artificial intelligence and robotics that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky and others in the 1980s. Moravec wrote in 1988, "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".
The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).
Sociotechnology is the study of processes on the intersection of society and technology. Vojinović and Abbott define it as "the study of processes in which the social and the technical are indivisibly combined". Sociotechnology is an important part of socio-technical design, which is defined as "designing things that participate in complex systems that have both social and technical aspects".
Emotional intelligence (EI) involves using cognitive and emotional abilities to function in interpersonal relationships, social groups as well as manage one's emotional states. It consists of abilities such as social cognition, empathy and also reasoning about the emotions of others.