A probabilistic logic network (PLN) is a conceptual, mathematical, and computational approach to uncertain inference; inspired by

logic programming Logic programming is a programming paradigm which is largely based on formal logic Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of log ...

, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances,

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 r ...

software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction,

abduction Abduction may refer to: Media Film and television * "Abduction" (''The Outer Limits''), a 2001 television episode * " Abduction" (''Death Note'') a Japanese animation television series * " Abductions" (''Totally Spies!''), a 2002 episode of an ...

, analogy, fuzziness and speculation, and reasoning about time and

causality Causality (also referred to as causation, or cause and effect) is influence by which one event, process, state, or object (''a'' ''cause'') contributes to the production of another event, process, state, or object (an ''effect'') where the ca ...

. PLN was developed by Ben Goertzel, Matt Ikle, Izabela Lyon Freire Goertzel, and Ari Heljakka for use as a cognitive algorithm used by MindAgents within the OpenCog Core. PLN was developed originally for use within the Novamente Cognition Engine.

Goal

The basic goal of PLN is to provide reasonably accurate probabilistic inference in a way that is compatible with both

term logic In philosophy, term logic, also known as traditional logic, syllogistic logic or Aristotelian logic, is a loose name for an approach to formal logic that began with Aristotle and was developed further in ancient history mostly by his followers, t ...

and

predicate logic First-order logic—also known as predicate logic, quantificational logic, and first-order predicate calculus—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quanti ...

and scales up to operate in real-time on large dynamic

knowledge base A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. ...

s. The goal underlying the theoretical development of PLN has been the creation of practical software systems carrying out complex, useful inferences based on uncertain knowledge and drawing uncertain conclusions. PLN has been designed to allow basic probabilistic inference to interact with other kinds of inference such as intensional inference, fuzzy inference, and higher-order inference using quantifiers, variables, and combinators, and be a more convenient approach than Bayesian networks (or other conventional approaches) for the purpose of interfacing basic probabilistic inference with these other sorts of inference. In addition, the inference rules are formulated in such a way as to avoid the paradoxes of

Dempster–Shafer theory The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory (DST), is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and i ...

Implementation

PLN begins with a term logic foundation and then adds on elements of

probabilistic Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, ...

and combinatory logic, as well as some aspects of predicate logic and autoepistemic logic, to form a complete inference system, tailored for easy integration with software components embodying other (not explicitly logical) aspects of intelligence. PLN represents

truth value In logic and mathematics, a truth value, sometimes called a logical value, is a value indicating the relation of a proposition to truth, which in classical logic has only two possible values ('' true'' or ''false''). Computing In some prog ...

s as intervals, but with different semantics than in imprecise probability theory. In addition to the interpretation of truth in a probabilistic fashion, a truth value in PLN also has an associated amount of ''certainty''. This generalizes the notion of truth values used in autoepistemic logic, where truth values are either known or unknown and when known, are either true or false. The current version of PLN has been used in narrow-AI applications such as the inference of biological hypotheses from knowledge extracted from biological texts via language processing, and to assist the

reinforcement learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

of an embodied agent, in a simple

virtual world A virtual world (also called a virtual space) is a computer-simulated environment which may be populated by many users who can create a personal avatar, and simultaneously and independently explore the virtual world, participate in its activitie ...

, as it is taught to play "fetch".

References

* {{cite book , authors = Ben Goertzel, Matthew Iklé, Izabela Lyon Freire Goertzel, Ari Heljakka , title = Probabilistic Logic Networks: A Comprehensive Conceptual, Mathematical and Computational Framework for Uncertain Inference , url = https://archive.org/details/probabilisticlog00goer , url-access = limited , publisher = Springer , year = 2008 , isbn = 978-0-387-76871-7 , pages
333

External links

OpenCog Wiki (GNU-FDL)
Artificial intelligence Probabilistic arguments Non-classical logic

Goal

Implementation

References

See also

External links