Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.
Artificial Intelligence (AI) is one of the developing areas in computer science that aims to design and develop intelligent machines that can demonstrate higher level of resilience to complex decision-making environments. The computations that at any time make it possible to assist users to perceive, reason, and act forms the basis for effective Artificial Intelligence in any given computational device (e.g. computers, robotics etc.,).
This makes it clear that the AI in a given environment can be accomplished only through the simulation of the real-world scenarios into logical cases with associated reasoning in order to enable the computational device to deliver the appropriate decision for the given state of the environment. This makes it clear that reasoning is one of the key elements that contribute to the collection of computations for AI. The effectiveness of the reasoning in the world of AI has a significant level of bearing on the ability of the machine to interpret and react to the environmental status or the problem it is facing.
Reasoning is deemed as the key logical element that provides the ability for human interaction in a given social environment. The key aspect associated with reasoning is the fact that the perception of a given individual is based on the reasons derived from the facts that relative to the environment as interpreted by the individual involved. This makes it clear that in a computational environment involving electronic devices or machines, the ability of the machine to deliver a given reason depends on the extent to which the social environment is quantified as logical conclusions with the help of a reason or combination of reasons.
The major aspect associated with reasoning is that in case of human reasoning the reasoning is accompanied with introspection which allows the individual to interpret the reason through self-observation and reporting of consciousness. This naturally provides the ability to develop the resilience to exceptional situations in the social environment thus providing a non-feeble minded human to react in one way or other to a given situation that is unique in its nature in the given environment.
It is also critical to appreciate the fact that the reasoning in the mathematical perspective mainly corresponds to the extent to which a given environmental status can be interpreted using probability in order to help predict the reaction or consequence in any given situation through a sequence of actions.
In the case of uncertainty in the environment, that challenges the normal reasoning approach to derive a specific conclusion or decision by the individual involved. The introspective nature developed in humans and some animals provides the ability to cope with the uncertainty in the environment. This adaptive nature of the non-feeble minded human is the key ingredient that provides the ability to interpret the reasons to a given situation as opposed to merely following the logical path that results through the reasoning process.
Reasoning is deemed to be one of the key components to enable effective artificial programs in order to tackle complex decision-making problems using machines. This is naturally because of the fact that the logical path followed by a program to derive a specific decision is mainly dependent on the ability of the program to handle exceptions in the process of delivering the decision. This naturally makes it clear that the effective use of the logical reasoning to define the past, present and future states of the given problem alongside the plausible exception handlers is the basis for successfully delivering the decision for a given problem in chosen environment.
This is the area of computer programming under Artificial Intelligence that faces the major challenge of enabling the effective decision-making by machines. The key aspect associated with the adaptive software development is the need for effective identification of the various exceptions and the ability to enable dynamic exception handling based on a set of generic rules. The concept of fuzzy matching and de-duplication that are popular in case of software tools used for cleansing data cleansing in the business environment follow the above-mentioned concept of adaptive software.
This is the case there the ability of the software to decide the best possible outcome for a given situation is programmed using a basic set of directory rules that are further enhanced using references to a variety of combinations that comprise the database of logical combinations for reasons that can be applied to a given situation.
The concept of fuzzy matching is also deemed to be a major breakthrough in the implementation of adaptive programming of machines and computing devices in Artificial Intelligence. This is naturally because of the fact that the ability of the program to not only refer to a set of rules and associated reference but also to interpret the combination of reasons derived relative to the given situation prior to arriving on a specific decision.
The effective development of adaptive software for an AI device in order to perform effective decision-making in the given environment mainly depends on the extent to which the software is able to interpret the reasons prior to deriving the decision. The adaptive software programming in artificial intelligence is not only deemed as an area of challenge but also the one with extensive scope for development to enable the simulation of complex real-world problems using Artificial Intelligence.
It is also critical to appreciate the fact that the adaptive software programming in the case of Artificial Intelligence is mainly focused on the ability to not only identify and interpret the reasons using a set of rules and combination of outcomes but also to demonstrate a degree of introspection. In other words the adaptive software in case of Artificial Intelligence is expected to enable the device to become a learning machine as opposed to an efficient exception handler. This further opens room for exploring into knowledge management as part of the AI device to accomplish a certain degree of introspection similar to that of a non-feeble minded human.
This area of Artificial Intelligence can be deemed to be a derivative of the adaptive software whereby the speech/audio stream captured by the device deciphers the message for performs the appropriate task. The speech recognition in the AI field of science poses key issues of matching, reasoning to enable access control/ decision-making and exception handling on top of the traditional issues of noise filtering and isolation of the speaker’s voice for interpretation. The case of speech recognition is where the aforementioned issues are faced whilst in case of speech synthesis using computers, the major issue is the decision-making as the decision through the logical reasoning alone can help produce the appropriate response to be synthesised into speech by the machine.
The speech synthesis as opposed to speech recognition depends only on the adaptive nature of the software involved. This is due to the fact that the reasons derived form the interpretation of the input captured using the decision-making rules and combinations for fuzzy matching form the basis for the actual synthesis of the sentences that comprises the speech. The grammar associated with the sentences so framed and its reproduction depends heavily on the initial decision of the adaptive software using the logical reasons identified for the given environmental situation. Hence the complexity of speech synthesis and recognition poses a great challenge for effective reasoning in Artificial Intelligence.
This is deemed to be yet another key challenge faced by Artificial Intelligence programming using reasoning. This is because of the fact that neural networks aim to implement the local behaviour observed by the human brain. The layers of perception and the level of complexity associated through the interaction between different layers of perception alongside decision-making through logical reasoning.
The computation of the decision using the neural networks strategy is aimed to solving highly complex problems with a greater level of external influence due to uncertainties that interact with each other or demonstrate a significant level of dependency to one another. The adaptive software approach to the development of the reasoned decision-making in machines forms the basis for neural networks with a significant level complexity and dependencies involved.
The Single Layer Perceptions (SLP) and the representation of Boolean expressions using SLPs makes it clear that the effective deployment of the neural networks can help simulate complex problems and also provide the ability to develop resilience within the machine. The learning capability and the extent to which the knowledge management can be incorporated as a component in the AI machine can be defined successfully through identification and simulation of the SLPs and their interaction with each other in a given problem environment.
The case of neural networks also opens the possibility of handling multi-layer perceptions as part of adaptive software programming through independently programming each layer before enabling interaction between the layers as part of the reasoning for the decision-making. The key influential element for the aforementioned is the ability of the programmer(s) to identify the key input and output components for generating the reasons to facilitate the decision-making.
The backpropagation or backward error propagation algorithm deployed in the neural networks is a salient feature that helps achieve the major aspect of learning from mistakes and errors in a given computer program. The backpropagation algorithm in the multi-layer networks is one of the major areas where the adaptive capabilities of the AI application program can be strengthened to reflect the real-world problem solving skills of the non-feeble minded human.
The neural networks implementation of AI applications can be achieved to a sustainable level using the backpropagation error correction technique. This self-correcting and learning system using the neural networks approach is one of the major elements that can help implement complex problems’ simulation using AI applications. The case of reasoning in the light of the neural networks proves that the effective use of the layer-based approach to simulate the problems in order to allow for the interaction will help achieve reliable AI application development methodologies.
Reasoning is one of the major elements that can help simulate real-world problems using computers or robotics regardless of the complexity of the problems.