Qualitative Reasoning (QR) is an area of research within Artificial Intelligence (AI) that automates reasoning about continuous aspects of the physical world, such as space, time, and quantity, for the purpose of problem solving and planning using qualitative rather than quantitative information.[1] Precise numerical values or quantities are avoided, and qualitative values are used instead (e.g., high, low, zero, rising, falling, etc.).[2]

Purpose

Qualitative reasoning creates non-numerical descriptions of physical systems and their behavior, preserving important behavioral properties and qualitative distinctions.[3] The goal of qualitative reasoning research is to develop representation and reasoning methods that enable computer programs to reason about the behavior of physical systems, without precise quantitative information. An example is observing pouring rain and the steadily rising water level of a river, which is sufficient information to take action against possible flooding without knowing the exact water level, the rate of change, or the time the river might flood.[4]

Principles

The principles used are motivated by human cognition.

The principles of qualitative reasoning include:[5]

  • Discrete values
    • Represent continuous quantities using discrete entities for reasoning
    • Example: Instead of using a numerical value for rate of change, consider whether it is increasing, decreasing or constant
  • Relevant values
    • Choose qualitative values based on relevance to a task
    • Example: If the temperature is changing, the boiling point may be important, but if the temperature is constant, the boiling point may be irrelevant
  • Ambiguous values or results
    • Instead of providing one answer, provide a range of answers
    • Example: Instead of computing a numeric level or quantity of water, provide two answers: low or zero
  • Modeling a process
    • Represent the states
    • Represent the transitions between states
    • For quantities, determine landmarks and use inequality reasoning
    • Example:
      If the temperature of water is below the boiling point, then the water level is constant or slowly decreasing;
      if the temperature of water is above the boiling point, then the water level is rapidly decreasing;
      if water has a temperature that changes from below the boiling point to above the boiling point, then the water level will change to rapidly decreasing;
      if water is above the boiling point for a specified length of time, the water level will be low or zero

Uses

The techniques which have been developed for qualitative reasoning permit the simulation of quantitative systems which are subject to multiple constraints in the form of inequalities as well as equalities. It can permit the simulation of certain important systems, such as ecosystems, which might otherwise be too complex to model. Qualitative reasoning provides a method for modeling with quantitative inequalities in addition to qualities.

Successful application areas include process control, system verification, explanation,[2] autonomous spacecraft support, simulation and explanation of the behavior of structures,[6] failure analysis and on-board diagnosis of vehicle systems, automated generation of control software for photocopiers, conceptual knowledge capture in ecology, and intelligent aids for human learning.[3]

See also

References

  1. ↑ "Qualitative Reasoning: Reaching Good Conclusions without Being Precise". Association for the Advancement of Artificial Intelligence (AAAI).
  2. 1 2 John Daintith (2004). A Dictionary of Computing. Oxford University Press. ISBN 0198608772.
  3. 1 2 Bert Bredeweg and Peter Struss (2003). "Current Topics in Qualitative Reasoning" (PDF). American Association for Artificial Intelligence.
  4. ↑ Yumi Iwasaki (May–June 1997). "Real-World Applications of Qualitative Reasoning". IEEE Expert: Intelligent Systems. Knowledge Systems Laboratory, Department of Computer Science: Stanford University.
  5. ↑ "Qualitative Reasoning, CS227" (PDF). Stanford University. 2011.
  6. ↑ Salvaneschi, Paolo; Cadei, Mauro; Lazzari, Marco (1997). "A causal modelling framework for the simulation and explanation of the behaviour of structures". Artificial Intelligence in Engineering. 11 (3): 205–216. doi:10.1016/S0954-1810(96)00040-4.
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