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INTELLIGENT CONTROLLER. UNIT I. INTRODUCTION. Intelligent control is a class of control techniques that use various AI computing approaches. Intelligent control can be divided into the following major sub-domains:. Neural network control Bayesian control Fuzzy (logic) control
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INTELLIGENT CONTROLLER UNIT I
INTRODUCTION • Intelligent control is a class of control techniques that use various AI computing approaches. • Intelligent control can be divided into the following major sub-domains: • Neural network control • Bayesian control • Fuzzy (logic) control • Neuro-fuzzy control • Expert Systems • Genetic control • Intelligent agents (Cognitive/Conscious control)
Expert System • An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. • Expert systems are most common in a specific problem domain. • Methods for simulating the performance of the expert are 1) The creation of a so-called "knowledgebase" which uses some knowledge representation formalism to capture the Subject Matter Expert's (SME) knowledge 2) A process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering.
Intelligent Agents (Cognitive / Conscious Control) • Intelligent agents may also learn or use knowledge to achieve their goals. • Example: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal. • It is an autonomous entity in AI which observes and acts upon an environment (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational).
Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally APPROACHES TO INTELLIGENT CONTROL • The field of artificial intelligence, or AI, attempts to understand intelligent entities whose definitions as described by books gives us four possible goals to pursue in artificial intelligence: • A human-centered approach must be an empirical science, involving hypothesis and experimental confirmation. • A rationalist approach involves a combination of mathematics and engineering.
Acting humanly: The Turing Test approach • Proposed by Alan Turing (Turing, 1950) • Designed to provide a satisfactory operational definition of intelligence. • Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. • Roughly speaking, the test he proposed is that the computer should be interrogated by a human via a teletype, and passes the test if the interrogator cannot tell if there is a computer or a human at the other end. • Avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence. • The issue of acting like a human comes up primarily when AI programs have to interact with people, as when an expert system explains how it came to its diagnosis, or a natural language processing system has a dialogue with a user. These programs must behave according to certain normal conventions of human interaction in order to make themselves understood.
Thinking humanly: The cognitive modelling approach • If a given program thinks like a human, we need to determine how humans think. • Two ways to do this: • through introspection--trying to catch our own thoughts as they go by • through psychological experiments. • The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind. • We will simply note that AI and cognitive science continue to fertilize each other, especially in the areas of vision, natural language, and learning.
Thinking rationally: The laws of thought approach • These laws of thought were supposed to govern the operation of the mind, and initiated the field of logic. • The development of formal logic provided a precise notation for statements about all kinds of things in the world and the relations between them. • By 1965, programs existed that could, given enough time and memory, take a description of a problem in logical notation and find the solution to the problem, if one exists. The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems.
Acting rationally: The rational agent approach • Acting rationally means acting so as to achieve one's goals, given one's beliefs. • An agent is just something that perceives and acts. • In this approach, AI is viewed as the study and construction of rational agents.
AI APPROACHES CYBERNETICS AND BRAIN SIMULATION ( CYBERNETICS AND COMPUTATIONAL NEUROSCIENCE ) • There is no consensus on how closely the brain should be simulated. • In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics. • Some of them built machines that used electronic networks to exhibit rudimentary intelligence. • By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.
AI APPROACHES SYMBOLIC ( GOOD OLD FASHIONED ARTIFICIAL INTELLIGENCE ) When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. Cognitive simulation • Economist Herbert Simon and Alan Newell studied human problem solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. • Their research team performed psychological experiments to demonstrate the similarities between human problem solving and the programs.
AI APPROACHES SYMBOLIC ( GOOD OLD FASHIONED ARTIFICIAL INTELLIGENCE ) Logic based • John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms. • Used formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. "Anti-logic" or "scruffy" • Marvin Minsky and Seymour Papert found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. • Roger Schank described their "anti-logic" approaches as "scruffy". Commonsense knowledge bases are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.
AI APPROACHES SYMBOLIC ( GOOD OLD FASHIONED ARTIFICIAL INTELLIGENCE ) Knowledge based • When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.
AI APPROACHES SUB - SYMBOLIC Bottom-up, embodied, situated, behavior-based or nouvelle AI • Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s and reintroduced the use of control theory in AI. These approaches are also conceptually related to the embodied mind thesis. Computational Intelligence • Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle 1980s. These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence
AI APPROACHES STATISTICAL • In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific sub problems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. • The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).
RULE BASED SYSTEMS • The simplest form of artificial intelligence, which is generally used in industry, is the rule-based system, also known as the expert system. • A rule-based system is a way of encoding a human expert's knowledge in a fairly narrow area into an automated system. • The knowledge of the expert is captured in a set of rules, each of which encodes a small piece of the expert's knowledge. Each rule has a left hand side and a ride hand side. The left hand side contains information about certain facts and objects, which must be true in order for the rule to potentially, fire (that is, execute). • Any rules whose left hand sides match in this manner at a given time are placed on an agenda. One of the rules on the agenda is picked (there is no way of predicting which one), and its right hand side is executed, and then it is removed from the agenda. The agenda is then updated (generally using a special algorithm called the Rete algorithm), and a new rules is picked to execute. This continues until there are no more rules on the agenda.
KNOWLEDGE REPRESENTATION • Knowledge representation is an area in artificial intelligence that is concerned with how to formally "think", that is, how to use a symbol system to represent "a domain of discourse" - that which can be talked about, along with functions that may or may not be within the domain of discourse that allow inference (formalized reasoning) about the objects within the domain of discourse to occur. • Generally speaking, some kind of logic is used both to supply a formal semantics of how reasoning functions apply to symbols in the domain of discourse, as well as to supply (depending on the particulars of the logic), operators such as quantifiers, modal operators, etc. that, along with an interpretation theory, give meaning to the sentences in the logic. • When we design a knowledge representation (and a knowledge representation system to interpret sentences in the logic in order to derive inferences from them) we have to make trades across a number of design spaces, described in the following sections.
