Commonsense reasoning

Commonsense reasoning is one of the branches of artificial intelligence (AI) that is concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day. These assumptions include judgments about the physical properties, purpose, intentions and behavior of people and objects, as well as possible outcomes of their actions and interactions. A device that exhibits commonsense reasoning will be capable of predicting results and drawing conclusions that are similar to humans' folk psychology (humans' innate ability to reason about people's behavior and intentions) and naive physics (humans' natural understanding of the physical world).See also: Logic machines in fiction and List of fictional computers Commonsense reasoning is one of the branches of artificial intelligence (AI) that is concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day. These assumptions include judgments about the physical properties, purpose, intentions and behavior of people and objects, as well as possible outcomes of their actions and interactions. A device that exhibits commonsense reasoning will be capable of predicting results and drawing conclusions that are similar to humans' folk psychology (humans' innate ability to reason about people's behavior and intentions) and naive physics (humans' natural understanding of the physical world). In Artificial intelligence, commonsense knowledge is the set of background information that an individual is intended to know or assume and the ability to use it when appropriate. It is a shared knowledge (between everybody or people in a particular culture or age group only). The way to obtain commonsense is by learning it or experiencing it. In communication, it is what people don’t have to say because the interlocutor is expected to know or make a presumption about. The commonsense knowledge problem is a current project in the sphere of artificial intelligence to create a database that contains the general knowledge most individuals are expected to have, represented in an accessible way to artificial intelligence programs that use natural language. Due to the broad scope of the commonsense knowledge this issue is considered to be among the most difficult ones in the AI research sphere. In order for any task to be done as a human mind would manage it, the machine is required to appear as intelligent as a human being. Such tasks include object recognition, machine translation and text mining. To perform them, the machine has to be aware of the same concepts that an individual, who possess commonsense knowledge, recognizes. In 1961, Bar Hillel first discussed the need and significance of practical knowledge for natural language processing in the context of machine translation. Some ambiguities are resolved by using simple and easy to acquire rules. Others require a broad acknowledgement of the surrounding world, thus they require more commonsense knowledge. For instance when a machine is used to translate a text, problems of ambiguity arise, which could be easily resolved by attaining a concrete and true understanding of the context. Online translators often resolve ambiguities using analogous or similar words. For example, in translating the sentences 'The electrician is working' and 'The telephone is working' into German, the machine translates correctly 'working' in the means of 'laboring' in the first one and as 'functioning properly' in the second one. The machine has seen and read in the body of texts that the German words for 'laboring' and 'electrician' are frequently used in a combination and are found close together. The same applies for 'telephone' and 'function properly'. However, the statistical proxy which works in simple cases often fails in complex ones. Existing computer programs carry out simple language tasks by manipulating short phrases or separate words, but they don’t attempt any deeper understanding and focus on short-term results. Issues of this kind arise in computer vision. For instance when looking at the photograph of the bathroom (figure 1) some of the items that are small and only partly seen, such as the towels or the body lotions, are recognizable due to the surrounding objects (toilet, wash basin, bathtub), which suggest the purpose of the room. In an isolated image they would be difficult to identify. Movies prove to be even more difficult tasks. Some movies contain scenes and moments that cannot be understood by simply matching memorized templates to images. For instance, to understand the context of the movie, the viewer is required to make inferences about characters’ intentions and make presumptions depending on their behavior. In the contemporary state of the art, it is impossible to build and manage a program that will perform such tasks as reasoning, i.e. predicting characters’ actions. The most that can be done is to identify basic actions and track characters. The need and importance of commonsense reasoning in autonomous robots that work in a real-life uncontrolled environment is evident. For instance, if a robot is programmed to perform the tasks of a waiter on a cocktail party, and it sees that the glass he had picked up is broken, the waiter-robot should not pour liquid into the glass, but instead pick up another one. Such tasks seem obvious when an individual possess simple commonsense reasoning, but to ensure that a robot will avoid such mistakes is challenging. Significant progress in the field of the automated commonsense reasoning is made in the areas of the taxonomic reasoning, actions and change reasoning, reasoning about time. Each of these spheres has a well-acknowledged theory for wide range of commonsense inferences.