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Behavior Modelling
In artificial intelligence, a behavior selection algorithm, or action selection algorithm, is an algorithm that selects appropriate behaviors or actions for one or more intelligent agents. In game artificial intelligence, it selects behaviors or actions for one or more non-player characters. Common behavior selection algorithms include: *Finite-state machines ** Hierarchical finite-state machines * Decision trees *Behavior trees * Hierarchical task networks *Hierarchical control systems *Utility systems *Dialogue tree (for selecting what to say) Related concepts In application programming, run-time selection of the behavior of a specific method is referred to as the strategy design pattern. See also * Cognitive model - all cognitive models exhibit behavior in terms of making decisions (taking action), making errors, and with various reaction times. * Behavioral modeling, in systems theory * Behavioral modeling in hydrology * Behavioral modeling in computer-aided design * Be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Strategy Design Pattern
In computer programming, the strategy pattern (also known as the policy pattern) is a behavioral software design pattern that enables selecting an algorithm at runtime. Instead of implementing a single algorithm directly, code receives run-time instructions as to which in a family of algorithms to use. Strategy lets the algorithm vary independently from clients that use it. Strategy is one of the patterns included in the influential book ''Design Patterns'' by Gamma et al. that popularized the concept of using design patterns to describe how to design flexible and reusable object-oriented software. Deferring the decision about which algorithm to use until runtime allows the calling code to be more flexible and reusable. For instance, a class that performs validation on incoming data may use the strategy pattern to select a validation algorithm depending on the type of data, the source of the data, user choice, or other discriminating factors. These factors are not known until run-t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Game Artificial Intelligence
In video games, artificial intelligence (AI) is used to generate responsive, adaptive or intelligent behaviors primarily in non-player characters (NPCs) similar to human-like intelligence. Artificial intelligence has been an integral part of video games since their inception in the 1950s. AI in video games is a distinct subfield and differs from academic AI. It serves to improve the game-player experience rather than machine learning or decision making. During the golden age of arcade video games the idea of AI opponents was largely popularized in the form of graduated difficulty levels, distinct movement patterns, and in-game events dependent on the player's input. Modern games often implement existing techniques such as pathfinding and decision trees to guide the actions of NPCs. AI is often used in mechanisms which are not immediately visible to the user, such as data mining and procedural-content generation. In general, game AI does not, as might be thought and sometimes i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Weak AI
Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of mind, or, as narrow AI, is focused on one narrow task. In John Searle, John Searle's terms it “would be useful for testing hypotheses about minds, but would not actually be minds”. Weak artificial intelligence focuses on mimicking how humans perform basic actions such as remembering things, perceiving things, and solving simple problems. As opposed to strong AI, which uses technology to be able to think and learn on its own. Computers are able to use methods such as algorithms and prior knowledge to develop their own ways of thinking like human beings do. Strong artificial intelligence systems are learning how to run independently of the programmers who programmed them. Weak AI is not able to have a mind of its own, and can only imitate physical behaviors that it can observe. It is contrasted with Strong AI, which is defined variously as: * Artificial general intelligence: a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Synthetic Intelligence
Synthetic intelligence (SI) is an alternative/opposite term for artificial intelligence emphasizing that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence. John Haugeland proposes an analogy with simulated diamonds and synthetic diamonds—only the synthetic diamond is truly a diamond. Synthetic means that which is produced by synthesis, combining parts to form a whole; colloquially, a human-made version of that which has arisen naturally. A "synthetic intelligence" would therefore be or appear human-made, but not a simulation. Definition The term was used by Haugeland in 1986 to describe artificial intelligence research up to that point, which he called " good old fashioned artificial intelligence" or "GOFAI". AI's first generation of researchers firmly believed their techniques would lead to real, human-like intelligence in machines. After the first AI winter, many AI researchers shifted their focus from arti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Model-based Reasoning
In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. With this approach, the main focus of application development is developing the model. Then at run time, an "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction. Reasoning with declarative models A robot and dynamical systems as well are controlled by software. The software is implemented as a normal computer program which consists of if-then-statements, for-loops and subroutines. The task for the programmer is to find an algorithm which is able to control the robot, so that it can do a task. In the history of robotics and optimal control there were many paradigm developed. One of them are expert systems, which is focused on restricted domains. Expert systems are the precursor to model based systems. The main reason why model-based reasoning is researched since the 1990s is t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Case-based Reasoning
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent type of analogy solution making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deepl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Behavioral Modeling Language
A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure. Overview A modeling language can be graphical or textual. * ''Graphical'' modeling languages use a diagram technique with named symbols that represent concepts and lines that connect the symbols and represent relationships and various other graphical notation to represent constraints. * ''Textual'' modeling languages may use standardized keywords accompanied by parameters or natural language terms and phrases to make computer-interpretable expressions. An example of a graphical modeling language and a corresponding textual modeling language is EXPRESS. Not all modeling languages are executable, and for those that are, the use of them doesn't necessarily mean that programmers are no longer required. On the contrary, executabl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Behavioral Modeling In Computer-aided Design
In computer-aided design, behavioral modeling is a high-level circuit modeling technique where behavior of logic is modeled. The Verilog-AMS and VHDL-AMS VHDL-AMS is a derivative of the hardware description language VHDL (IEEE standard 1076-1993). It includes analog and mixed-signal extensions (AMS) in order to define the behavior of analog and mixed-signal systems (IEEE 1076.1-1999). The VHDL-AMS ... languages are widely used to model logic behavior. Other modeling approaches * RTL Modeling : logic is modeled at register level. * Structural Modeling : logic is modeled at both register level and gate level. References ''Analog Behavioral Modeling with the Verilog-A Language'' by Dan FitzPatrick, Ira Miller. Computer-aided design {{comp-sci-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Behavioral Modeling In Hydrology
In hydrology, behavioral modeling is a modeling approach that focuses on the modeling of the behavior of hydrological systems. The behavioral modeling approach makes the main assumption that every system, given its environment, has a most probable behavior. This most probable behavior can be either determined directly based on the observable system characteristics and expert knowledge or, the most frequent case, has to be inferred from the available information and a likelihood function that encodes the probability of some assumed behaviors. This modeling approach has been proposed by Sivapalan et al. (2006) in watershed hydrology. See also * Ecohydrology * Geomorphology * Biogeomorphology * Fluvial landforms of streams Fluvial processes have made streams, stream beds, and river valleys which have various classifications. Classification There are five generic classifications: *Consequent streams are streams whose course is a direct consequence of the original s ... Refere ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Behavioral Modeling
The behavioral approach to systems theory and control theory was initiated in the late-1970s by J. C. Willems as a result of resolving inconsistencies present in classical approaches based on state-space, transfer function, and convolution representations. This approach is also motivated by the aim of obtaining a general framework for system analysis and control that respects the underlying physics. The main object in the behavioral setting is the behavior – the set of all signals compatible with the system. An important feature of the behavioral approach is that it does not distinguish a priority between input and output variables. Apart from putting system theory and control on a rigorous basis, the behavioral approach unified the existing approaches and brought new results on controllability for nD systems, control via interconnection,J.C. Willems On interconnections, control, and feedback IEEE Transactions on Automatic Control Volume 42, pages 326-339, 1997 Available online ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
