Post on 04-Jan-2016
Simulating Emergent Cognition in Artificial Life
Júlio L. R. Monteiro (Ph.D student)Advisor: Marcio Lobo NettoUniversity of São Paulo - BrazilCognitio Research Group
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Summary
1. Introduction2. Objectives3. Methodology4. Environmental Model5. Creature Model6. Cognition Model7. Expected Results
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1. Introduction
Life can be understood as:Open, associative systemSelf-organized, autonomousEvolutive, learning from past experiencesHierarchical, with many complexity levels
Life is a system to preserve information against natural decay[ADAMI’88]
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2. Objectives
Observe the emergence and evolution of complex cognitive processes in virtual life creatures, such as:Learning from experienceDevelopment of strategies (planning)Abstraction of concepts Attention
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3. Methodology
Develop an interactive computer simulator:Simple but extensible universe modelArtificial life creatures, with virtual DNAEvolutive cognitive model
Design experiments and observe “state shift” situations
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4. Environmental Model
3D universe Basic entities are colored geometric solids Basic Properties:
Color (visible state)Energy (internal state)Shape (function)Mass (integrity, inertia)
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Universal Dynamics
MovementCollisionGravityEnergy conversion (via Effectors)
Energy TransferEmittersReceptors
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5. Creature Model
A creature develops many subsystems: Conjunctive Perceptive Effective Cognitive Reproductive
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Conjunctive System
Implements the creature’s main body Holds information related to:
Energy reservesPhysical integritySockets to other subsystems
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Perceptive System
Responsible for the identification of other entities and their attributes
Typical perceptors:ColorShapeEnergyDistance
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Effective System
Allows creatures to interact with the environment
Typical effectors:MovementEnergy emittersGrapplers
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Cognitive System
Allows complex control of behavior Filters the input from the Perceptive system Builds an internal representation of the universe Relays commands to the Effective System Implemented using the Memory Evolutive
System [EHRESMANN’02]
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Reproductive System
Allows the production of other entities or creatures
Creatures have Metastrings as virtual DNA Many Metastrings can be stored together
and used at different stages
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Metastrings
A special kind of meta-entities with no volume or mass, that represents recipes for building any possible entity
Uses hierarchical categories [EILENBERG’45]
Can be as detailed as needed Mutation occurs more frequently in lower
hierarchical levels
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Metastring example
CREATURE
EFFECTOR_2
EFFECTOR_1
PERCEPTOR_1
SHAPE
SOCKET_3
SOCKET_2
SOCKET_1
COGNITOR
EFFECTIVE
PERCEPTIVE
COGNITIVE
PERCECTOR_2
CONJUNTIVE
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6. Cognition Model
Based on the Memory Evolutive System model[EHRESMANN’02]
Described as a category graph with Interconnected agents in various hierarchical complexity levels
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MES and Complexity
Agents in a higher level have a representation as a distinguished pattern in the lower level (colimit)
The existence of multi-fold objects justifies implies complex links that can’t be expressed in lower levels
[EHRESMANN’05]
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MES in Detail
Composed of local hierarchical Centers of Regulation (CRs)
Each CRs operates in different timescales, developing a stepwise process: Formation of the actual landscape Selection of a strategy based on the Memory Building an anticipated landscape Command effectors to realize the strategy Evaluate and memorize the results
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7. Expected Results
Some points of interest are:Formation of a multilayered memory:
Empirical (storing all sensorial stimuli) Experiential (storing causal relations) Procedural (storing recombined strategies) Semantic (allows abstraction of concepts)
Group behavior (competition / alliance)Design of a genetic language
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Expected Results
The chosen model allows for the gradual increase in detail in the description of the environment
Evolution can be measured in species and creature memory
Precise experimental setups still need to be formulated
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References
ADAMI, C. (1988) Introduction to Artificial Life, Springer, New York.
EHRESMANN, A.; VANBREMEERSCH, J.-P. (2002) Emergence Processes up to Consciousness Using the Multiplicity Principle and Quantum Physics. In: Proc. AIP Conference, V. 627, I. 1, pp. 221-233
EHRESMANN, A.; VANBREMEERSCH, J.-P. (2005) Memory Evolutive Systems Homepage, Amiens, FR: http://perso.wanadoo.fr/vbm-ehr/, visited in July, 10, 2005
EILENBERG, S.; MAC LANE, S. (1945) General theory of natural equivalences. In: Trans. AMS 58, p. 231-294.