TY - GEN
T1 - Bounded seed-AGI
AU - Nivel, Eric
AU - Thórisson, Kristinn R.
AU - Steunebrink, Bas R.
AU - Dindo, Haris
AU - Pezzulo, Giovanni
AU - Rodríguez, Manuel
AU - Hernández, Carlos
AU - Ognibene, Dimitri
AU - Schmidhuber, Jürgen
AU - Sanz, Ricardo
AU - Helgason, Helgi P.
AU - Chella, Antonio
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84905860537&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-09274-4_9
DO - 10.1007/978-3-319-09274-4_9
M3 - Conference contribution
AN - SCOPUS:84905860537
SN - 9783319092737
VL - 8598 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 96
BT - Artificial General Intelligence - 7th International Conference, AGI 2014, Proceedings
PB - Springer
T2 - 7th International Conference on Artificial General Intelligence, AGI 2014
Y2 - 1 August 2014 through 4 August 2014
ER -