**1. Introduction**

The recent technological contest between technology giants, such as Google and Microsoft, includes their competition in the creation of chatbots based on statistically created large language models (LLMs) using huge text bases of electronically available texts. The beginnings of the idea of using the interaction of a chatbot with humans as a test of artificial Intelligence (AI) is attributed to Alan Turing and particularly to his famous 1950 paper [1] where he proposed the so-called "Turing test." The Turing test aspires to discover which of the responding entities is human and which is not.

If a system is misjudged by a jury that it is a human, then it is acknowledged to be intelligent. This is, therefore, an operational method of defining artificial intelligence whose declarative definition of universal acceptance still evades us.

Chatbot technology is a branch of the wider field of natural language question answering (QA) for the development of which many AI researchers have been

working since the late fifties, and the earliest paper on the subject, it seems that was published in 1961, namely [2]. Both Turing test contestants and the latest LLM-based chatbots seem to have mostly ignored all the huge research work done on QA during the many decades since 1961. They seem oblivious of the recent developments that concentrate on explainable question answering systems that their operation usually requires some form of machine consciousness.

For these reasons, a good way to test chatbots is to submit questions that require elementary machine consciousness for answering them correctly and generating correct explanations. A convenient way of testing is proposed here namely "analysis dialogs" that are formulated by the tester having in mind to test the presence of rudimentary machine consciousness as briefly reviewed in the next section.

### **2. Machine consciousness**

Machine consciousness is a new subfield of AI. This subfield emerged at about 2004 with a self-aware system workshop held in the USA and has since developed rapidly in the USA, in Europe, and recently in China.

In 2009, the first scientific journal with the name "International Journal of Machine Consciousness" started, but in 2020, its name was changed to "Journal of Artificial Intelligence and Consciousness" The concept of machine consciousness has created strong controversies. Some scientists strongly oppose to the idea of an artificial system that is able to exhibit behavior that only living beings can do. As a result of the relevant discussions, several issues have emerged.

Some of the issues involved in machine consciousness are:


My position is that there is nothing wrong in defining a category of AI systems that display patterns of behavior inspired from the behavior of living conscious beings. One such pattern that I have studied is the one of "reporting" the steps followed, while performing logical reasoning that may be useful for explaining to the user of such a system why an answer is given to her question.

The seemingly hard problem of testing "machine consciousness" may, hence, be simplified by considering all information available to a computer system about itself. The only information about its operation that a computer system can use may belong to one of the two cases:


We, hereby, propose that, by definition, machine consciousness of operation can have no other meaning. Consciousness of structure is another possible source of selfreferring information, but it can be associated with a computer system once and for all by its designer, while a) and b) above are dynamically generated and are influenced by the input from the environment and stored appropriately.

Interestingly the logical programming language Prolog may be thought of as a way of eliminating the need for writing algorithms. "Programs" written in Prolog are declarative descriptions of problems, and hence differ from algorithmic programs that implement a special algorithm for the solution of a problem. Prolog "programs" consist of facts and rules that decompose relations to simpler relations. These "programs" that are more akin to problem descriptions are either interpreted or compiled into machine code by a single general-purpose algorithm. Therefore, for any system exhibiting machine consciousness and implemented in Prolog, it would be sufficient that its corresponding subsystems are connected to this general-purpose algorithm. This method of implementation of systems with machine consciousness greatly facilitates the relevant design.

This method may be based on a tracing mechanism present at least in Turbo-Prolog but in some other newer Prologs too. If the trace of operations is too long and incomprehensible to the user. Techniques for presenting summaries of these traces to the user adapted to her personal preferences, as well as for generating explanations that increase the faith of the user to the results of a computer system may be easily developed.

Machine consciousness is useful for implementing critical applications such as for defense and medical systems. The implementation of bug-free computer systems is possibly another field of application of it. Programmers understand less and less all the possible results of the programs they write as their complexity rises above a certain level. This is dangerous if these programs not only control critical infrastructure systems such as air traffic control systems, power stations, and energy grids but also systems such as airplanes and trains. It is very urgent then that a new kind of software engineering be developed for the implementation of computer systems that "know themselves" and can give crucial answers to the "what if" and "why" questions of their users in cases of emergency or failure. Artificial intelligence can be of help with methods resulting from research results in the field of machine consciousness.

### **3. Explainable question answering**

Machine consciousness is a prerequisite for a kind of question answering namely "explainable question answering" that has recently emerged as a hot topic.

In such systems, users may demand them to be trustworthy and convincing that their output is correct. Trust may be enhanced if explanations are generated that

support the truth of an output from a modern computer system. However, in a quite old paper of mine [3] the implementation of an early explainable question answering from texts system called ARISTA is described. This paper presents results of experiments in knowledge engineering with scientific texts.

The application of the ARISTA method that stands for "automatic representation independent syllogistic text analysis" uses natural language text as a knowledge base in contrast with the methods followed by the then prevailing approach, which relied on the translation of texts into some knowledge representation formalism. The experiments demonstrate the feasibility of deductive question answering and explanation generation directly from texts involving mainly causal reasoning. Illustrative examples of the operation of a prototype based on the ARISTA method and implemented in Prolog are presented in that paper.

A more modern system that can claim to use "machine consciousness" for explainable question answering is presented in [4] called AMYNTAS. The system AMYNTAS was implemented in Prolog.

AMYNTAS consists of six modules implemented as separate programs totaling about 50 pages of code. These modules communicate through some temporary files that store intermediate results. The six modules are the question processing module, the text pre-processing module, the ontology extraction module, the shallow parsing or text chunking module, the question answering module, and the metagnostic processing module that generates explanations of its operation. This explainability capacity of AMYNTAS makes it qualify as exhibiting machine consciousness.

The question processing module extracts information from the input question. The information extracted is a list consisting of the entities mentioned in the question and the relations that connect them. For example, in the question "what influences p53" the entities are the protein p53 and the "blank" entity standing for the unknown entity that is sought and the relation is "influence."

The text pre-processing module represents each word of a sentence as a fact with three arguments the first being the word itself, the second being the identifier of the sentence, and the third being the position of the word in the sentence counting from left to right. The modules of AMYNTAS are described below.

The ontology extraction module locates linguistic patterns in the input text corpus that may be used to extract automatically meronymic and taxonomic knowledge that may be used at question answering time.

The shallow parsing or text chunking module locates a verb representing the main relation mentioned in the input question and extracts the two substrings of the text of the sentence being analyzed that appear to the left and the right of the verb and end at some stop-word or punctuation mark. The sentence analyzed is the source of the answer.

The question answering module finds the answer to the question from the preprocessed text. The question answering module accepts questions that potentially require the combination of facts with the use of prerequisite knowledge for answering them.

The prerequisite knowledge available to our system includes ontological knowledge, inference rules, and synonyms of the named entities involved of the domain, which used in order to combine two or more facts mentioned in the text corpus.

At question answering time three looping operations are taking place. The basic loop concerns the search for an entity in a chunk related to the relation of the question. The second loop concerns the transformation of the list obtained from the question by following a particular strategy from the explicit list given to the system. The third loop searches for chains of facts using the matching of named entities occurring in the right part of one fact and the left part of another fact.

Another area of application that we have applied explainability is that of the AI anti-drone defense systems. In [5] an AI decision support system is proposed that may support a human supervising such a system. The human supervisor has the duty of approving or rejecting the proposals for mitigating alien drones of the AI anti-drone defense system considering the explanations generated of the proposal.

The explanation may be of a multimedia nature. Multimedia rhetoric relations are utilized in the generation of multimedia explanations. These new rhetorical relations proposed connect textual parts with parts of images.

The images are obtained by sensors whose outputs may be fused for inspection of the defense situations. The decisions of the AI system use the vulnerability of the targets of the attack. The explains its proposals in attempting to protect the most vulnerable targets. The explanations make use of multimedia rhetoric relations.

### **4. The Loebner Prize**

The Loebner Prize contest of artificial intelligence is the first formal Turing test. In 1990 H. Loebner agreed with The Cambridge Center for Behavioral Studies to underwrite a contest designed to implement the Turing test. Dr. Loebner pledged a Grand Prize of \$100,000 and a gold medal for the first computer program whose responses were indistinguishable from a human's. Such a computer program can be said "to think." Each year an annual cash prize and a bronze medal were awarded to the most humanlike computer program. The winner or champion of the annual contest was the best entry relative to other entries of that year, irrespective of how good it was in an absolute sense.

The Loebner Prize contest was first inaugurated in 1991 at The Computer Museum (Boston, USA) and has since been hosted at many different locations. The 2012 Turing Centenary Loebner Prize competition took place on May 15th, 2012 at the Bletchley Park Museum.
