Explainable artficial intelligence planning

Explainable Artificial Intelligence being an offshoot of Artificial Intelligence, aims at equipping the next generation of intelligent machi...

Explainable Artificial Intelligence being an offshoot of Artificial Intelligence, aims at equipping the next generation of intelligent machines with the ability to explain their actions, decisions, and rationales. This development owes to the grave danger of perversion and complexity of machines in human environments. The set tasks for these machines demand a certain level of transparency and understanding. The unpredictability of these machines is such that the safety and welfare of the human race are put at risk in the hands of robots that are characterized by opaque, unrelated needed information in the system of human-computer dialogue. Furthermore, the intent and rationale of these machines are rather anonymous or classified; they need to some extent, explicitness in their decisions. Therefore, while planning explainable artificial intelligence machines/computers/robots, one should consider these opinions and see to it that they are checked and in a long-term, balanced.

In an address to these issues raised, the notions of planning an XAI machine or a generation of it, strategies are realized, actions sequences are set, and provisions are made for a machine to be able to convey information as to why it took a particular course of action and not the other. According to research, XAI planning is a field of/on its own but remains a branch of Artificial Intelligence. As a field, it focuses on devising the set techniques and processing, realizing their nature typically for the execution by intelligent machines, automated robots, and unmanned computer systems. With the help of quantum computing and other resources, these machines are classified and according to their problems or shortcomings, and they are given or administered with a complexity of solutions that will hence aide optimal performance and explainability. XAI is then developed with different decision theories for models to be at their best performance rates. XAI planning is done in line with two salient and indispensable parameters; explicability and predictability. These two are termed the notions of XAI planning. Explicability is assuring for a unique and original mode of operation by robots delivering for humans and doing so with adequate feedback and explanation as to the why question of actions. Humans need to be rest assured that the decisions of these robots or machines are resolving and final. On the other hand, predictability forms the conviction of action on the part of naturally intelligent humans. Being that XAI is to be developed and established; humans are given the room to be able to predict actions or decisions that robots that are in mutual understanding with them will execute. The human-computer relationship allows for the human to proffer the likeliest of actions that the machines will perform and also state the reason for such an undertaking. In computing both measures( explicability and predictability), it is postulated that humans will be able to comprehend the plans of intelligent machines by associating abstract tasks with robot performances; which is known as the labeling process. Learning the set of schemes used by these intelligent entities, a new plan is labeled so as to compute and organize the next AI machine’s explicability and predictability. These measures can be used by intelligent machines which have turned explainable intelligence machines to proactively choose or directly synthesize plans and decisions that are far more explicable and predictable than that of the preceding generation(s) and to the human race.

Plans for XAI incorporate a series of ideas and instruction principles that will go to extents in enabling a machine to state the reason or reasons for its courses of actions. However, in the bid to making this marrying of plans become a mélange of helpful odds, there are series of likely problems one might encounter. The most basic planning setback is a single scenario of the overall s-t reachability problem for simply represented transition graphs, which hold a handful of other importance in applications in Computer Aided Verification, Intelligent Control, discrete event system diagnosis, etc.  In this process also termed automated planning and scheduling, XAI robots are programmed to have human-friendly data and information that will make the world quite a better place, even if the age of machines attempts to choke it of its life. In known vicinities with the right supply of types of equipment, planning can be done offline, and solutions can be singled out momentarily and evaluated before execution kicks in. Quite contrarily, in some uncertain environments, XAI planning strategies are necessarily done online. The needed and devised models and policies need to be adapted on the spot. In such situations, the complexities of solutions usually take the form of trial and error processes until breakthrough as is prominently characteristic of Artificial Intelligence. This set of solutions being encompassing involves effective programming reinforcement learning and combinatorial optimization. They are described and instructed with a language called action language.

The XAI program at Darpa is aimed to create suite of machine learning techniques that could serve two main purposes:
To produce explainable models besides maintaining higher level of the learning performance (accuracy in predictions)
To enable human users to have complete trust, and managing the new emerging generation of the artificially intelligent partners

Explaining the predictions means to present the visual or textual artifacts which can provide the qualitative understanding about the relationship among the instances of a component such as patches in some image or words in some text as well as the model’s prediction. If the predictions can be properly explainable, so it would help human users to trust as well as use the machine learning technique more effectively only if the explanations are intelligible and faithful.

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Ninicue : -D | Review & Research Blog: Explainable artficial intelligence planning
Explainable artficial intelligence planning
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