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Bounded Seed-AGI

  • Eric Nivel
  • Kristinn R. Thórisson
  • Bas R. Steunebrink
  • Haris Dindo
  • Giovanni Pezzulo
  • Manuel Rodríguez
  • Carlos Hernández
  • Dimitri Ognibene
  • Jürgen Schmidhuber
  • Ricardo Sanz
  • Helgi P. Helgason
  • Antonio Chella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8598)

Abstract

Four principal features of autonomous control systems are left both unaddressed and unaddressable by present-day engineering methodologies: (1) The ability to operate effectively in environments that are only partially known at design time; (2) A level of generality that allows a system to re-assess and re-define the fulfillment of its mission in light of unexpected constraints or other unforeseen changes in the environment; (3) The ability to operate effectively in environments of significant complexity; and (4) The ability to degrade gracefully—how it can continue striving to achieve its main goals when resources become scarce, or in light of other expected or unexpected constraining factors that impede its progress. We describe new methodological and engineering principles for addressing these shortcomings, that we have used to design a machine that becomes increasingly better at behaving in underspecified circumstances, in a goal-directed way, on the job, by modeling itself and its environment as experience accumulates. The work provides an architectural blueprint for constructing systems with high levels of operational autonomy in underspecified circumstances, starting from only a small amount of designer-specified code—a seed. Using value-driven dynamic priority scheduling to control the parallel execution of a vast number of lines of reasoning, the system accumulates increasingly useful models of its experience, resulting in recursive self-improvement that can be autonomously sustained after the machine leaves the lab, within the boundaries imposed by its designers. A prototype system named AERA has been implemented and demonstrated to learn a complex real-world task—real-time multimodal dialogue with humans—by on-line observation. Our work presents solutions to several challenges that must be solved for achieving artificial general intelligence.

Keywords

General Intelligence Composite State Goto Step Core Operation Instantiate Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eric Nivel
    • 1
  • Kristinn R. Thórisson
    • 1
    • 3
  • Bas R. Steunebrink
    • 2
  • Haris Dindo
    • 4
  • Giovanni Pezzulo
    • 6
  • Manuel Rodríguez
    • 5
  • Carlos Hernández
    • 5
  • Dimitri Ognibene
    • 6
  • Jürgen Schmidhuber
    • 2
  • Ricardo Sanz
    • 5
  • Helgi P. Helgason
    • 3
  • Antonio Chella
    • 4
  1. 1.Icelandic Institute for Intelligent Machines, IIIMIceland
  2. 2.The Swiss AI Lab IDSIA, USI & SUPSISwitzerland
  3. 3.CADIAReykjavik UniversityIceland
  4. 4.DINFOUniversità degli studi di PalermoItaly
  5. 5.ASLABUniversidad Politécnica de MadridSpain
  6. 6.ISTCConsiglio Nazionale delle RicercheItaly

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