Probabilistic logic

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The aim of a probabilistic logic (also probability logic and probabilistic reasoning) is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal argument. The result is a richer and more expressive formalism with a broad range of possible application areas. Probabilistic logics attempt to find a natural extension of traditional logic truth tables: the results they define are derived through probabilistic expressions instead. A difficulty with probabilistic logics is that they tend to multiply the computational complexities of their probabilistic and logical components. Other difficulties include the possibility of counter-intuitive results, such as those of Dempster-Shafer theory. The need to deal with a broad variety of contexts and issues has led to many different proposals.

Historical context[edit]

There are numerous proposals for probabilistic logics. Very roughly, they can be categorized into two different classes: those logics that attempt to make a probabilistic extension to logical entailment, such as Markov logic networks, and those that attempt to address the problems of uncertainty and lack of evidence (evidentiary logics).

That probability and uncertainty are not quite the same thing may be understood by noting that, despite the mathematization of probability in the Enlightenment, mathematical probability theory remains, to this very day, entirely unused in criminal courtrooms, when evaluating the "probability" of the guilt of a suspected criminal.[1]

More precisely, in evidentiary logic, there is a need to distinguish the truth of a statement from the confidence in its truth: thus, being uncertain of a suspect's guilt is not the same as assigning a numerical probability to the commission of the crime. A single suspect may be guilty or not guilty, just as a coin may be flipped heads or tails. Given a large collection of suspects, a certain percentage may be guilty, just as the probability of flipping "heads" is one-half. However, it is incorrect to take this law of averages with regard to a single criminal (or single coin-flip): the criminal is no more "a little bit guilty" than a single coin flip is "a little bit heads and a little bit tails": we are merely uncertain as to which it is. Conflating probability and uncertainty may be acceptable when making scientific measurements of physical quantities, but it is an error, in the context of "common sense" reasoning and logic. Just as in courtroom reasoning, the goal of employing uncertain inference is to gather evidence to strengthen the confidence of a proposition, as opposed to performing some sort of probabilistic entailment.

Historically, attempts to quantify probabilistic reasoning date back to antiquity. There was a particularly strong interest starting in the 12th century, with the work of the Scholastics, with the invention of the half-proof (so that two half-proofs are sufficient to prove guilt), the elucidation of moral certainty (sufficient certainty to act upon, but short of absolute certainty), the development of Catholic probabilism (the idea that it is always safe to follow the established rules of doctrine or the opinion of experts, even when they are less probable), the case-based reasoning of casuistry, and the scandal of Laxism (whereby probabilism was used to give support to almost any statement at all, it being possible to find an expert opinion in support of almost any proposition.).[1]

Modern proposals[edit]

Below is a list of proposals for probabilistic and evidentiary extensions to classical and predicate logic.

  • The term "probabilistic logic" was first used in a paper by Nils Nilsson published in 1986, where the truth values of sentences are probabilities.[2] The proposed semantical generalization induces a probabilistic logical entailment, which reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1. This generalization applies to any logical system for which the consistency of a finite set of sentences can be established.
  • The central concept in the theory of subjective logic[3] are opinions about some of the propositional variables involved in the given logical sentences. A binomial opinion applies to a single proposition and is represented as a 3-dimensional extension of a single probability value to express various degrees of ignorance about the truth of the proposition. For the computation of derived opinions based on a structure of argument opinions, the theory proposes respective operators for various logical connectives, such as e.g. multiplication (AND), comultiplication (OR), division (UN-AND) and co-division (UN-OR) of opinions [4] as well as conditional deduction (MP) and abduction (MT).[5]
  • Approximate reasoning formalism proposed by fuzzy logic can be used to obtain a logic in which the models are the probability distributions and the theories are the lower envelopes.[6] In such a logic the question of the consistency of the available information is strictly related with the one of the coherence of partial probabilistic assignment and therefore with Dutch book phenomenon.
  • Markov logic networks implement a form of uncertain inference based on the maximum entropy principle—the idea that probabilities should be assigned in such a way as to maximize entropy, in analogy with the way that Markov chains assign probabilities to finite state machine transitions.
  • Systems such as Pei Wang's Non-Axiomatic Reasoning System (NARS) or Ben Goertzel's Probabilistic Logic Networks (PLN) add an explicit confidence ranking, as well as a probability to atoms and sentences. The rules of deduction and induction incorporate this uncertainty, thus side-stepping difficulties in purely Bayesian approaches to logic (including Markov logic), while also avoiding the paradoxes of Dempster-Shafer theory. The implementation of PLN attempts to use and generalize algorithms from logic programming, subject to these extensions.
  • In the theory of probabilistic argumentation,[7][8] probabilities are not directly attached to logical sentences. Instead it is assumed that a particular subset of the variables involved in the sentences defines a probability space over the corresponding sub-σ-algebra. This induces two distinct probability measures with respect to , which are called degree of support and degree of possibility, respectively. Degrees of support can be regarded as non-additive probabilities of provability, which generalizes the concepts of ordinary logical entailment (for ) and classical posterior probabilities (for ). Mathematically, this view is compatible with the Dempster-Shafer theory.
  • The theory of evidential reasoning[9] also defines non-additive probabilities of probability (or epistemic probabilities) as a general notion for both logical entailment (provability) and probability. The idea is to augment standard propositional logic by considering an epistemic operator K that represents the state of knowledge that a rational agent has about the world. Probabilities are then defined over the resulting epistemic universe Kp of all propositional sentences p, and it is argued that this is the best information available to an analyst. From this view, Dempster-Shafer theory appears to be a generalized form of probabilistic reasoning.
  • The SP theory of intelligence[10][11] and its realisation in the SP computer model exhibits several forms of reasoning all of which are fundamentally probabilistic. These include: one-step deductive reasoning; chains of reasoning; abductive reasoning; reasoning with probabilistic networks and trees; reasoning with rules; nonmonotonic reasoning and reasoning with default values; Bayesian reasoning with explaining away; causal reasoning; reasoning that is not supported by evidence; and inheritance of attributes in class-inclusion hierarchies and part-whole hierarchies. There is also potential for spatial reasoning and for what-if reasoning. This versatility all flows from the central role in the SP system of the powerful concept of multiple alignment, a concept which has been borrowed and adapted from the concept of Multiple sequence alignment in bioinformatics. This framework allows the several forms of reasoning to work together flexibly in any combination. There is further information, with download links for papers, on The SP theory of intelligence.

Possible application areas[edit]

See also[edit]


  1. ^ a b James Franklin, The Science of Conjecture: Evidence and Probability before Pascal, 2001 The Johns Hopkins Press, ISBN 0-8018-7109-3
  2. ^ Nilsson, N. J., 1986, "Probabilistic logic," Artificial Intelligence 28(1): 71-87.
  3. ^ Jøsang, A., 2001, "A logic for uncertain probabilities," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(3):279-311.
  4. ^ Jøsang, A. and McAnally, D., 2004, "Multiplication and Comultiplication of Beliefs," International Journal of Approximate Reasoning, 38(1), pp.19-51, 2004
  5. ^ Jøsang, A., 2008, "Conditional Reasoning with Subjective Logic," Journal of Multiple-Valued Logic and Soft Computing, 15(1), pp.5-38, 2008.
  6. ^ Gerla, G., 1994, "Inferences in Probability Logic," Artificial Intelligence 70(1–2):33–52.
  7. ^ Kohlas, J., and Monney, P.A., 1995. A Mathematical Theory of Hints. An Approach to the Dempster-Shafer Theory of Evidence. Vol. 425 in Lecture Notes in Economics and Mathematical Systems. Springer Verlag.
  8. ^ Haenni, R, 2005, "Towards a Unifying Theory of Logical and Probabilistic Reasoning," ISIPTA'05, 4th International Symposium on Imprecise Probabilities and Their Applications: 193-202. [1]
  9. ^ Ruspini, E.H., Lowrance, J., and Strat, T., 1992, "Understanding evidential reasoning," International Journal of Approximate Reasoning, 6(3): 401-424.
  10. ^ Wolff, J. G., 2013, "The SP theory of intelligence: an overview", Information, 4(3): 283-341.
  11. ^ Wolff, J. G., 2016, "The SP theory of intelligence: distinctive features and advantages", IEEE Access, 4, 216-246.

Further reading[edit]

  • Adams, E. W., 1998. A Primer of Probability Logic. CSLI Publications (Univ. of Chicago Press).
  • Bacchus, F., 1990. "Representing and reasoning with Probabilistic Knowledge. A Logical Approach to Probabilities". The MIT Press.
  • Carnap, R., 1950. Logical Foundations of Probability. University of Chicago Press.
  • Chuaqui, R., 1991. Truth, Possibility and Probability: New Logical Foundations of Probability and Statistical Inference. Number 166 in Mathematics Studies. North-Holland.
  • Haenni, H., Romeyn, JW, Wheeler, G., and Williamson, J. 2011. Probabilistic Logics and Probabilistic Networks, Springer.
  • Hájek, A., 2001, "Probability, Logic, and Probability Logic," in Goble, Lou, ed., The Blackwell Guide to Philosophical Logic, Blackwell.
  • Jaynes, E., ~1998, "Probability Theory: The Logic of Science", pdf and Cambridge University Press 2003.
  • Kyburg, H. E., 1970. Probability and Inductive Logic Macmillan.
  • Kyburg, H. E., 1974. The Logical Foundations of Statistical Inference, Dordrecht: Reidel.
  • Kyburg, H. E. & C. M. Teng, 2001. Uncertain Inference, Cambridge: Cambridge University Press.
  • Romeiyn, J. W., 2005. Bayesian Inductive Logic. PhD thesis, Faculty of Philosophy, University of Groningen, Netherlands. [2]
  • Williamson, J., 2002, "Probability Logic," in D. Gabbay, R. Johnson, H. J. Ohlbach, and J. Woods, eds., Handbook of the Logic of Argument and Inference: the Turn Toward the Practical. Elsevier: 397-424.

External links[edit]