In
information technology a reasoning system is a
software system that generates conclusions from available
knowledge using
logical techniques such as
deduction and
induction. Reasoning systems play an important role in the implementation of
artificial intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
and
knowledge-based systems.
By the everyday usage definition of the phrase, all computer systems are reasoning systems in that they all automate some type of logic or decision. In typical use in the
Information Technology field however, the phrase is usually reserved for systems that perform more complex kinds of reasoning. For example, not for systems that do fairly straightforward types of reasoning such as calculating a sales tax or customer discount but making logical inferences about a medical diagnosis or mathematical theorem. Reasoning systems come in two modes: interactive and batch processing. Interactive systems interface with the user to ask clarifying questions or otherwise allow the user to guide the reasoning process. Batch systems take in all the available information at once and generate the best answer possible without user feedback or guidance.
Reasoning systems have a wide field of application that includes
scheduling,
business rule processing,
problem solving
Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business an ...
,
complex event processing Event processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), and deriving a conclusion from them. Complex event processing, or CEP, consists of a set of concepts and techniques ...
,
intrusion detection,
predictive analytics,
robotics,
computer vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human ...
, and
natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to proc ...
.
History
The first reasoning systems were theorem provers, systems that represent axioms and statements in First Order Logic and then use rules of logic such as
modus ponens to infer new statements. Another early type of reasoning system were general problem solvers. These were systems such as the
General Problem Solver designed by
Newell and
Simon
Simon may refer to:
People
* Simon (given name), including a list of people and fictional characters with the given name Simon
* Simon (surname), including a list of people with the surname Simon
* Eugène Simon, French naturalist and the genus ...
. General problem solvers attempted to provide a generic planning engine that could represent and solve structured problems. They worked by decomposing problems into smaller more manageable sub-problems, solving each sub-problem and assembling the partial answers into one final answer. Another example general problem solver was the
SOAR family of systems.
In practice these theorem provers and general problem solvers were seldom useful for practical applications and required specialized users with knowledge of logic to utilize. The first practical application of
automated reasoning
In computer science, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer prog ...
were
expert system
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert.
Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if� ...
s. Expert systems focused on much more well defined domains than general problem solving such as medical diagnosis or analyzing faults in an aircraft. Expert systems also focused on more limited implementations of logic. Rather than attempting to implement the full range of logical expressions they typically focused on modus-ponens implemented via IF-THEN rules. Focusing on a specific domain and allowing only a restricted subset of logic improved the performance of such systems so that they were practical for use in the real world and not merely as research demonstrations as most previous automated reasoning systems had been. The engine used for automated reasoning in expert systems were typically called
inference engines. Those used for more general logical inferencing are typically called
theorem provers.
With the rise in popularity of expert systems many new types of automated reasoning were applied to diverse problems in government and industry. Some such as case-based reasoning were off shoots of expert systems research. Others such as constraint satisfaction algorithms were also influenced by fields such as decision technology and linear programming. Also, a completely different approach, one not based on symbolic reasoning but on a connectionist model has also been extremely productive. This latter type of automated reasoning is especially well suited to pattern matching and signal detection types of problems such as text searching and face matching.
Use of logic
The term reasoning system can be used to apply to just about any kind of sophisticated
decision support system as illustrated by the specific areas described below. However, the most common use of the term reasoning system implies the computer representation of logic. Various implementations demonstrate significant variation in terms of
systems of logic
A formal system is an abstract structure used for inferring theorems from axioms according to a set of rules. These rules, which are used for carrying out the inference of theorems from axioms, are the logical calculus of the formal system.
A form ...
and formality. Most reasoning systems implement variations of
propositional and
symbolic (
predicate
Predicate or predication may refer to:
* Predicate (grammar), in linguistics
* Predication (philosophy)
* several closely related uses in mathematics and formal logic:
**Predicate (mathematical logic)
**Propositional function
**Finitary relation, o ...
) logic. These variations may be mathematically precise representations of formal logic systems (e.g.,
FOL), or extended and
hybrid
Hybrid may refer to:
Science
* Hybrid (biology), an offspring resulting from cross-breeding
** Hybrid grape, grape varieties produced by cross-breeding two ''Vitis'' species
** Hybridity, the property of a hybrid plant which is a union of two dif ...
versions of those systems (e.g., Courteous logic). Reasoning systems may explicitly implement additional logic types (e.g.,
modal,
deontic,
temporal logics). However, many reasoning systems implement imprecise and semi-formal approximations to recognised logic systems. These systems typically support a variety of procedural and semi-
declarative techniques in order to model different reasoning strategies. They emphasise pragmatism over formality and may depend on custom extensions and attachments in order to solve real-world problems.
Many reasoning systems employ
deductive reasoning to draw
inferences from available knowledge. These inference engines support forward reasoning or backward reasoning to infer conclusions via
modus ponens. The
recursive reasoning methods they employ are termed ‘
forward chaining’ and ‘
backward chaining
Backward chaining (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence applications ...
’, respectively. Although reasoning systems widely support deductive inference, some systems employ
abductive,
inductive,
defeasible and other types of reasoning.
Heuristics may also be employed to determine acceptable solutions to
intractable problems.
Reasoning systems may employ the
closed world assumption The closed-world assumption (CWA), in a formal system of logic used for knowledge representation, is the presumption that a statement that is true is also known to be true. Therefore, conversely, what is not currently known to be true, is false. Th ...
(CWA) or
open world assumption
In a formal system of logic used for knowledge representation, the open-world assumption is the assumption that the truth value of a statement may be true irrespective of whether or not it is ''known'' to be true. It is the opposite of the clo ...
(OWA). The OWA is often associated with
ontological knowledge representation and the
Semantic Web. Different systems exhibit a variety of approaches to
negation. As well as
logical or
bitwise complement
In computer programming, a bitwise operation operates on a bit string, a bit array or a binary numeral (considered as a bit string) at the level of its individual bits. It is a fast and simple action, basic to the higher-level arithmetic operat ...
, systems may support existential forms of strong and weak negation including
negation-as-failure and ‘inflationary’ negation (negation of non-
ground atoms). Different reasoning systems may support
monotonic
In mathematics, a monotonic function (or monotone function) is a function between ordered sets that preserves or reverses the given order. This concept first arose in calculus, and was later generalized to the more abstract setting of ord ...
or
non-monotonic reasoning,
stratification and other logical techniques.
Reasoning under uncertainty
Many reasoning systems provide capabilities for reasoning under
uncertainty. This is important when building
situated
{{dictionary
In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term ''situated'' is commonly used to refer to robots, but some researchers argue that software agents c ...
reasoning agents which must deal with uncertain representations of the world. There are several common approaches to handling uncertainty. These include the use of certainty factors,
probabilistic methods such as
Bayesian inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and ...
or
Dempster–Shafer theory, multi-valued (‘
fuzzy
Fuzzy or Fuzzies may refer to:
Music
* Fuzzy (band), a 1990s Boston indie pop band
* Fuzzy (composer) (born 1939), Danish composer Jens Vilhelm Pedersen
* ''Fuzzy'' (album), 1993 debut album by the Los Angeles rock group Grant Lee Buffalo
* "Fu ...
’) logic and various
connectionist
Connectionism refers to both an approach in the field of cognitive science that hopes to explain mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial int ...
approaches.
Types of reasoning system
This section provides a non-exhaustive and informal categorisation of common types of reasoning system. These categories are not absolute. They overlap to a significant degree and share a number of techniques, methods and
algorithm
In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...
s.
Constraint solvers
Constraint solvers solve
constraint satisfaction problem
Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constra ...
s (CSPs). They support
constraint programming
Constraint programming (CP) is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. In constraint programming, users declaratively state t ...
. A
constraint
Constraint may refer to:
* Constraint (computer-aided design), a demarcation of geometrical characteristics between two or more entities or solid modeling bodies
* Constraint (mathematics), a condition of an optimization problem that the solution ...
is a which must be met by any valid
solution to a problem. Constraints are defined declaratively and applied to
variables within given domains. Constraint solvers use
search
Searching or search may refer to:
Computing technology
* Search algorithm, including keyword search
** :Search algorithms
* Search and optimization for problem solving in artificial intelligence
* Search engine technology, software for findi ...
,
backtracking
Backtracking is a class of algorithms for finding solutions to some computational problems, notably constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it d ...
and
constraint propagation
In constraint satisfaction, local consistency conditions are properties of constraint satisfaction problems related to the consistency of subsets of variables or constraints. They can be used to reduce the search space and make the problem easier ...
techniques to find solutions and determine optimal solutions. They may employ forms of
linear and
nonlinear programming
In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints or the objective function are nonlinear. An optimization problem is one of calculation of the extrema (maxima, minima or ...
. They are often used to perform
optimization within highly
combinatorial
Combinatorics is an area of mathematics primarily concerned with counting, both as a means and an end in obtaining results, and certain properties of finite structures. It is closely related to many other areas of mathematics and has many app ...
problem spaces. For example, they may be used to calculate optimal scheduling, design efficient
integrated circuits or maximise productivity in a manufacturing process.
Theorem provers
Theorem provers use
automated reasoning
In computer science, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer prog ...
techniques to determine
proofs of mathematical theorems. They may also be used to verify existing proofs. In addition to academic use, typical applications of theorem provers include verification of the correctness of integrated circuits, software programs, engineering designs, etc.
Logic programs
Logic programs (LPs) are
software programs written using
programming languages whose
primitives and
expressions provide direct representations of constructs drawn from mathematical logic. An example of a general-purpose logic programming language is
Prolog
Prolog is a logic programming language associated with artificial intelligence and computational linguistics.
Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily ...
. LPs represent the direct application of logic programming to solve problems. Logic programming is characterised by highly declarative approaches based on formal logic, and has wide application across many disciplines.
Rule engines
Rule engines represent conditional logic as discrete rules. Rule sets can be managed and applied separately to other functionality. They have wide applicability across many domains. Many rule engines implement reasoning capabilities. A common approach is to implement
production systems to support forward or backward chaining. Each rule (‘production’) binds a conjunction of
predicate clauses to a list of executable actions.
At run-time, the rule engine matches productions against facts and executes (‘fires’) the associated action list for each match. If those actions remove or modify any facts, or assert new facts, the engine immediately re-computes the set of matches. Rule engines are widely used to model and apply
business rule A business rule defines or constrains some aspect of business. It may be expressed to specify an action to be taken when certain conditions are true or may be phrased so it can only resolve to either true or false. Business rules are intended to ass ...
s, to control
decision-making in automated processes and to enforce business and technical policies.
Deductive classifier
Deductive classifiers arose slightly later than rule-based systems and were a component of a new type of
artificial intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
knowledge representation tool known as
frame languages. A frame language describes the problem domain as a set of classes, subclasses, and relations among the classes. It is similar to the
object-oriented model. Unlike object-oriented models however, frame languages have a formal semantics based on first order logic.
They utilize this semantics to provide input to the deductive classifier. The classifier in turn can analyze a given model (known as an
ontology
In metaphysics, ontology is the philosophical study of being, as well as related concepts such as existence, becoming, and reality.
Ontology addresses questions like how entities are grouped into categories and which of these entities ex ...
) and determine if the various relations described in the model are consistent. If the ontology is not consistent the classifier will highlight the declarations that are inconsistent. If the ontology is consistent the classifier can then do further reasoning and draw additional conclusions about the relations of the objects in the ontology.
For example, it may determine that an object is actually a subclass or instance of additional classes as those described by the user. Classifiers are an important technology in analyzing the ontologies used to describe models in the
Semantic web.
Machine learning systems
Machine learning systems evolve their behavior over time based on
experience. This may involve reasoning over observed events or example data provided for training purposes. For example, machine learning systems may use
inductive reasoning to generate
hypotheses for observed facts. Learning systems search for generalised rules or functions that yield results in line with observations and then use these generalisations to control future behavior.
Case-based reasoning systems
Case-based reasoning
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems.
In everyday life, an auto mechanic who fixes an engine by recallin ...
(CBR) systems provide solutions to problems by analysing similarities to other problems for which known solutions already exist. Case-based reasoning uses the top (superficial) levels of similarity; namely, the object, feature, and value criteria. This differs case-based reasoning from
analogical reasoning in that analogical reasoning uses only the "deep" similarity criterion i.e. relationship or even relationships of relationships, and ned not find similarity on the shallower levels. This difference makes case-based reasoning applicable only among cases of the same domain because similar objects, features, and/or values must be in the same domain, while the "deep" similarity criterion of "relationships" makes analogical reasoning applicable cross-domains where only the relationships ae similar between the cases. CBR systems are commonly used in customer/
technical support and
call centre
A call centre ( Commonwealth spelling) or call center (American spelling; see spelling differences) is a managed capability that can be centralised or remote that is used for receiving or transmitting a large volume of enquiries by telephone ...
scenarios and have applications in
industrial manufacture,
agriculture
Agriculture or farming is the practice of cultivating plants and livestock. Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that enabled people to ...
,
medicine,
law
Law is a set of rules that are created and are enforceable by social or governmental institutions to regulate behavior,Robertson, ''Crimes against humanity'', 90. with its precise definition a matter of longstanding debate. It has been vari ...
and many other areas.
Procedural reasoning systems
A
procedural reasoning system (PRS) uses reasoning techniques to select
plan
A plan is typically any diagram or list of steps with details of timing and resources, used to achieve an objective to do something. It is commonly understood as a temporal set of intended actions through which one expects to achieve a goal.
...
s from a
procedural knowledge
Procedural knowledge (also known as knowing-how, and sometimes referred to as practical knowledge, imperative knowledge, or performative knowledge) is the knowledge exercised in the performance of some task. Unlike descriptive knowledge (also kno ...
base. Each plan represents a course of action for achievement of a given
goal. The PRS implements a
belief-desire-intention model by reasoning over facts (‘
belief
A belief is an attitude that something is the case, or that some proposition is true. In epistemology, philosophers use the term "belief" to refer to attitudes about the world which can be either true or false. To believe something is to take ...
s’) to select appropriate plans (‘
intentions’) for given goals (‘desires’). Typical applications of PRS include management, monitoring and
fault detection
Fault detection, isolation, and recovery (FDIR) is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be ...
systems.
References
{{Automated reasoning
Deductive reasoning
Problem solving
Automated reasoning
Inductive reasoning
Cognitive architecture
Rule engines
Expert systems
*
Automated theorem proving
Constraint programming
Applied machine learning