Reasoning in Artificial Intelligence

The 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 the case of human reasoning the reasoning is accompanied by introspection which allows the individual to interpret the reason through self-observation and reporting of consciousness. This naturally provides the ability to develop 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.

The aforementioned corresponds with 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. The reasoning in the case of Artificial Intelligence (AI) which aims to develop the aforementioned in the electronic devices to perform complex tasks with minimal human intervention is presented in the next section.

Reasoning in Artificial Intelligence

Reasoning in Artificial Intelligence

The 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 dependant 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 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 the chosen environment. The key areas of challenge in the case of reasoning are discussed below.

Adaptive Software – This is the area of computer programming under Artificial Intelligence that faces the major challenge of enabling effective decision-making by machines. The key aspect associated with 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 the 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 references but also to interpret the combination of reasons derived relative to the given situation prior to arriving at a specific decision. From the aforementioned, it is evident that 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. This makes it clear that the adaptive software programming in artificial intelligence is not only deemed as an area of the 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 the 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 knowledge management as part of the AI device to accomplish a certain degree of introspection similar to that of a non-feeble-minded human.

Speech Synthesis/Recognition – 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 the 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 synthesized into speech by the machine.

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 from 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 comprise 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.

Neural Networks – 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 behavior observed by the human brain. The layers of perception and the level of complexity associated with the interaction between different layers of perception alongside decision-making through logical reasoning. This makes it clear that the computation of the decision using the neural network’s strategy is aimed at 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 on one another. This makes it clear that the adaptive software approach to the development of reasoned decision-making in machines forms the basis for neural networks with a significant level of complexity and dependencies involved.

The Single Layer Perceptions (SLP) and the representation of Boolean expressions using SLPs further make 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.

From the aforementioned, it is clear that the neural network 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 discussed earlier 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.

The discussion presented also reveals that 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.

Issues in the philosophy of Artificial Intelligence

The first and foremost issue faces in the case of AI implementation of simulating complex problems of the real world is the need for replication of the real-world environment in the computer/artificial world for the device to compute the reasons and derive upon a decision. This is naturally due to the fact that the simulation process involved in the replication of the environment for the real-world problem cannot always account for exceptions that arise due to unique human behavior in the interaction process. The lack of this facility and the fact that the environment so created cannot alter itself fundamentally apart from being altered due to the change in the state of the entities interacting within the simulated environment makes it a major hurdle for effective AI application development.

Apart from the real-world environment replication, the issue faced by the AI programmers is the fact that the reasoning processes and the exhaustiveness of the reasoning are limited to the knowledge/skills of the analysts involved. This makes it clear that the process of reasoning depending upon a non-feeble-minded human’s response to a given problem in the real world varies from one individual to another. Hence the reasons that can be simulated into the AI application can only be the fundamental logical reasons and the complex derivation of the reasons’ combination which is dependant on the individual cannot be replicated effectively in a computer.

Finally, the case of reasoning in the world of Artificial Intelligence is expected to provide a mathematical combination to the delivery of the desired results which cannot be accomplished in many cases due to the uniqueness of the decision made by the non-feeble-minded individual involved. This poses a great challenge to the successful implementation of AI in computers and robotics especially for complex problems that have various possibilities to choose from as result.

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