Learning Rich Representations from Low-Level Sensors: Papers from the AAAI 2013 Workshop
The Construction of Reality in a Cognitive System
Michael S. P. Miller
Los Angeles, California USA
piagetmodeler@gmail.com
observation, coordination, regulation, and compensation.
These processes form the basis of the PAM-P2 cognitive
system (see Figure 2). Observation is the process of
receiving stimuli, called “observables”, from the
environment into the memory of the cognitive system.
Coordination is the process of adding additional inferences
to the observables. Coordination encompasses many
inferential processes including spreading activation, belief
propagation, association, induction, deduction, analogy,
and planning. All such processes of inference essentially
entail adding additional elements, called “coordinations”,
to memory. Regulation is the process of correcting failed
solutions
and
reinforcing
successful
solutions.
Compensation is the process of recovering from failed
solutions by inverting a solution’s action sequence or by
setting new goals to mitigate the undesired side-effects of
past failures. Regulation and compensation are driven by
“perturbations”—ephemeral
meta-propositions
that
represent disturbances such as urges, gaps in knowledge,
predictions, and solution attempts.
Artificial cognitive systems can be architected using
design patterns. The PAM-P2 architecture consists of nine
cognitive system design patterns (see Figure 1) as defined
in Patterns for Cognitive Systems (Miller 2012). These
design patterns differentiate PAM-P2, and were expressly
selected for the architecture because they support ontology
formation, reminding, motivation, reaction, divergent
problem
solving,
convergent
deliberation,
and
daydreaming. The other reviewed systems did not contain
as many design patterns as PAM-P2, and none contained
the compensation design pattern. The inclusion of the
observation, coordination, regulation, and compensation
design patterns was expressly motivated by Jean Piaget’s
work.
People are genetically endowed with a body having a
range of possible actions and a set of basic needs. Artificial
cognitive systems must be given a figurative “body”, such
as that of a real world robot or virtual world avatar, with
Abstract
Getting an embodied cognitive system to form a mental
model of its world is a challenging prospect. Most AI
systems leverage domains defined entirely by the system
designers—initial objects, relations, operations, and even
search control knowledge are often pre-specified. Building
autonomous systems that can bootstrap themselves using a
minimal domain definition is a critical research objective of
Developmental AI. PAM-P2, a domain agnostic cognitive
system, builds a world model using an initial set of user
specified primitive actions and homeostatic needs. As
sensory datasets are received, an ontology is formed and
used to derive situations, events, episodes, solutions,
problems, and predictions. An overview of the PAM-P2
architecture and knowledge representation is presented.
Introduction
The problems of how a cognitive system can automatically
construct a model of entities and relationships in its world,
and how a system can leverage such a self-constructed
model to form and execute solutions that address the
system’s goals are important to Developmental AI. The
challenge for the Piagetian Autonomous Modeler
(PAM-P2) is to address these problems in a domain
agnostic manner: using capabilities, requirements, and
perceptions provided to the system at runtime. This paper
highlights core differences between PAM-P2 and other
cognitive systems, justifies those differences, and outlines
how the system learns representations.
The Development of Thought
In The Development of Thought Piaget et al (1977; 1985)
describe how natural cognitive systems (e.g., people)
construct their world using four main processes:
Copyright © 2013, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
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capabilities (possible actions) and requirements (needs).
In PAM-P2, these capabilities and requirements are
provided at runtime by the body when the body first
connects to the system. Afterwards, whenever the body
performs an action requested by PAM-P2, the body returns
whether or not it failed to do the action. This feedback
allows the cognitive system to eventually learn the
conditions under which actions can be successfully
performed.
PAM-P2 has both internally generated and body
provided teleological (goal attainment) needs and body
provided homeostatic (goal maintenance) needs. As the
body runs, it forwards urges to the cognitive system. An
urge is a perturbation representing the disequilibrium of a
homeostatic need: the delta between the target value and
actual value of a homeostatic variable. Taken as a whole,
the collection of urges defines the total equilibrium of the
cognitive system. PAM-P2 learns action sequences (i.e.,
solutions) that eventually return the system’s homeostatic
needs to equilibrium. Both urges and action results
provide a designer-endowed intrinsic reinforcement.
The memory of PAM-P2 is a database of
“neural propositions”—constructive connectionist facts
(Miller 2013).
Observables, coordinations, and
perturbations form the strata of the memory (see Figure 2).
Constructing Reality
A model of the body and its real or virtual environment is
formed in PAM-P2 by interactions where Piaget’s
processes take place. PAM-P2 concurrently observes the
environment, makes inferences, adapts itself to the
outcomes of prior actions, sets goals, and executes new
actions. The interactions begin when the body forwards
sensory datasets to the cognitive system. Observation
processes transform the datasets into percept, urge, and
action result propositions. Coordination processes build the
ontology, spread activation, propagate beliefs, incorporate
ontology elements into solutions, and update predictions
and solution attempts with failure or success using the
action results. Reflection processes reward, mutate, and
recombine solutions and predictions according to their
failures or successes, use urge perturbations to guide the
attention of the system by creating new goals to eliminate
urges, select new goals to neutralize undesired outcomes,
and toggle daydreaming—the generation of forward
situations and hypothetical consequences.
Finally,
execution processes select the best available solutions to
address goals, and send the solutions’ actions to the body.
Figure 1. By surveying twenty four cognitive systems, ten cognitive system design patterns were discovered in (Miller 2012).
Observation – storing perceptions in a
memory (e.g., a cache, or persistent store).
Coordination – adding inferences (based on
perceptions or other inferences) to memory.
Reminding – storing and retrieving episodes
(consisting of perceptions, inferences, and
actions) to and from memory.
Reaction – a loop where perceptions drive
action execution.
Deliberation – a loop where plans are
formed, executed, and monitored based on
goals and perception.
Meta Control (Reflection) – monitoring
and modifying components at run time.
Motivation – reprioritizing goals based on
perceptions and homeostatic needs.
Simulation – forming an expected
hypothetical forward model.
Regulation –
reinforcement.
plan
correction
or
and
Compensation – plan inversion
and
selecting goals to fix unwanted side effects.
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Figure 2. PAM-P2 Components.
Observation Components
Perceiver – receives sensory datasets
Storer – puts propositions in memory
Retriever – fetches propositions
Viewpoint Generator – makes viewpoints
Current Viewpoint – gets default viewpoint
Memory – stores neural propositions
Memory Components
Situations – proposition conjunctions
Viewpoints – for hypothetical reasoning
Perturbations – urges, gaps, hypotheses
Needs – goals
Solutions – plans
Problems – impediment ascriptions
Coordination Components
Activators – spread activation
Propagators – spread belief
Predictors – create predictions
Inductors – infer types, cases, events
Associators – create relationships
Reasoners – create specific inferences
Correlators – determine failure or success
Solvers – create solutions for needs
Execution Components
Deliberator – selects solutions for needs
Reactor – selects reflex solutions
Executor – finds unmet needs, does actions
Reflection Components
Attention – reprioritizes needs
Regulators – correct or reinforce solutions
Compensators – invert solutions, set needs
Simulation Supervisor – toggles simulation
Action Inhibitor – supports simulation
Consolidator – forgets & automates
Prasan Samtani, John Byrne and the rest of the PAM-P2
group for their continued comments and support.
Conclusions and Future Work
The PAM-P2 prototype is in development along with the
mind server, a framework for connecting robots and
mobile devices to cognitive systems. The PREMISE data
manipulation and data query language will be used in the
system’s Storer and Retriever components. Experimental
domains from Henry Chaput (2004) and others will be
recreated to test the final system.
References
Chaput, H. 2004. The Constructivist Learning Architecture: A
Model of Cognitive Development for Robust Autonomous Robots.
2004. Technical Report TR04-34. Artificial Intelligence
Laboratory, The University of Texas at Austin. Austin, TX.
Miller, M. S. P. 2012. Patterns for Cognitive Systems. In
Proceedings of the Sixth International Conference on Complex,
Intelligent and Software Intensive Systems (CISIS). Palermo Italy:
642-647.
Acknowledgements
Miller, M. S. P. 2013. The Neural Proposition: Structures for
Cognitive Systems. In Proceedings of 2013 AAAI Spring
Symposium on Creativity and (Early) Cognitive Development:
44-50.
This author would like to thank Dr. Yvonne Miller,
Todd Kaufmann, Dr. Sheldon Linker, Dr. Frank Guerin,
Dr. Ghassan Azar, Dr. Roland Hausser, Dr. Marc Pickett,
Stefan Smollack, and Dr. Manuela Veloso for their
feedback on the system and papers. Special thanks to
Stuart Allen, Peter Danenberg, Ryan Hewitt,
Piaget J. and Roslin, A. 1977. The Development of Thought: The
Equilibration of Cognitive Structures. Viking Press.
Piaget, J.; Brown, T.; and Thampy K.J. 1985. Equilibration of
Cognitive Structures: The Central Problem of Intellectual
Development. The University of Chicago Press.
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