Artificial intelligence plays a vital role in the ethics and governance of AI, comprising their work, advocacy, and choice of employment. In the prevailing world of artificial intelligence, where most things are driven by technology and data, the need arises to automate any system or process to perform complex tasks and functions automatically in order to deliver optimal productivity.
AI problems are usually inaccurate, and the models proposed are often too complicated to be proved by proper arguments. The only way to comprehend and appraise a theory is by perceiving what comes next. To evaluate our discrepancies and disagreements, we write programs that are expected to reflect our theories. If these programs perform, our theories are not proven; now, we gain some insight into how they perform. When the programs don’t yield expected results, we become incompetent to program the theories, and we understand what we need to redefine. This becomes valid because of the “level” problem. When the theory defines what to do at a high level, we need to communicate to the machine what to do at a low level. The objective of this book is to give you a broad spectrum of generally used tools for programming artificial Intelligence theories: discrimination nets, agendas, deduction, data dependencies, backtracking, etc.
