Artificial Intelligence (Part-V)- Integrating the Approaches and Tools of AI

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The Different Approaches and Tools Used in Artificial Intelligence

Here we continue with the fifth part of our blog on artificial intelligence. Those who have missed our fourth part can read it from Here. It will help to connect with this fifth part of the blog discussing on the different approaches and tools used in the development of artificial intelligence. Let us explore the blog to read about these in more details. In words of Elon Musk: 

"The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most".  

Intelligent Agent Paradigm

An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.

Agent Architectures and Cognitive Architectures

Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. Some cognitive architectures are custom-built to solve a arrow problem; others, such as Soar, are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are hybrid intelligent systems that include both symbolic and sub-symbolic components.

Tools of Artificial Intelligence:

Some of the different tools used in the development of artificial intelligence with higher accuracy and better performance are-

i) Logic Programming and Automated Reasoning

Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning. Several different forms of logic are used in AI research. Propositional logic involves truth functions such as "or" and "not". First-order logic adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a "degree of truth" (between 0 and 1) to vague statements such as "Rohit is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences. Default logic, non-monotonic logic and circumscription are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logic; situation calculus, event calculus and fluent calculus (for representing events and time); causal calculus; belief calculus; and modal logic. Overall, qualitative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules or regulations.

Continued in the next blog...