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The Cognitive Paradigm in the
Artificial Intelligence Research
Antonio Lieto
Università di Torino, Dipartimento di Informatica, IT
ICAR-CNR, Palermo, IT
June 10, 2020, Kyiv-Mohyla National University
https://www.antoniolieto.net
Driving Questions
- What characterize cognitively inspired AI systems?
- What are examples of cognitively inspired AI systems?
- How do they differ from standard AI systems?
- How can cognitively inspired AI systems be used?
2
From Human to Artificial Cognition
3
Inspiration
From Human to Artificial Cognition
4
Inspiration
Why?
Humans (and/or other natural systems) are still,
by far, the best unmatched systems able to solve
a wide-range of problems
From Human to Artificial Cognition (and
back)
5
Inspiration
Explanation
“Natural/Cognitive” Inspiration and AI
Early AI
Cognitive or Biological Inspiration
for the Design of “Intelligent Systems”
M. Minsky
R. Shank
Modern AI
“Intelligence” in terms of
optimality of a performance
(narrow tasks)
mid‘80s
A. Newell
H. Simon
D. Rumhelart
J. McClelland
N. Wiener
Nowadays:
Renewed attention
“The gap between natural
and artificial
systems is still enormous”
(A. Sloman, AIC 2014).
7
Cognitivism Nouvelle AI
Focus on high level cognitive functions Mainly focused on perception
Assuming structured representations
(physical symbol system, Simon and
Newell, 1976)
Assuming unstructured representation
(e.g. such as neural networks etc.) and
also integration with symbolic
approaches.
Architectural Perspective (integration
and interaction of all cognitive functions)
System perspective (not necessary to
consider a whole architectural
perspective).
Inspiration from human cognition
(heuristic-driven approach)
Bio-inspired computing, bottom-up
approach (for learning etc.).
2 Main Perspectives
10
‘Cognition is a type of computation’
Intelligence as “symbolic manipulation”
Cognitivism & Symbolic Representations
High level of abstraction
Nouvelle AI and Connectionism
- Sub-symbolic representations (including deep nets) —-> LEARNING,
PERCEPTION, CATEGORIZATION.
Low-level of abstraction
Modern successful AI systems
10
IBM Watson
(symbolic)
Alpha Go (Deep Mind)
(connectionist)
A Matter of Levels
Both the “cognitivist” and “nouvelle” approach can realize,
in principio, “cognitive artificial systems” or “artificial
models of cognition” provided that their models operate
at the “right” level of description.
When a biologically/cognitively inspired computational system/
architecture has an explanatory power w.r.t. the natural system
taken as source of inspiration ?
Which are the requirements to consider in order to design a
computational model of cognition with an explanatory power?
Functionalist vs Structuralist Models 11
Functionalism
• Functionalism (introduced by H. Putnam) postulates a weak
equivalence between cognitive processes and AI procedures.
• AI procedures have the functional role (“function as”) of human
cognitive procedures.
• Equivalence on the functional macroscopic properties of a given
intelligent behaviour (based on the same input-output specification).
• Multiple realizability (cognitive functions can be implemented in
different ways).
• This should produce predictive models (given an input and a set of
procedures functionally equivalent to what is performed by cognitive
processes then one can predict a given output).
12
Problems with Functionalism
• If the equivalence is so weak it is not possible to
interpret the results of a system (e.g. interpretation of
the system failures…).
• A pure functionalist model (posed without structural
constraints) is a black box where a predictive model
with the same output of a cognitive process can be
obtained with no explanatory power.
13
Birds and Jets
- Both Birds and Jets can fly but a jet is not a good explanatory model
of a bird since its flights mechanisms are different from the mechanism
of bird.
- Purely functional models/systems are not “computational models of
cognition” (they have no explanatory power w.r.t. the natural system
taken as source of inspiration). 14
Modern “Functional” Systems in AI
They are very good artificial systems but they have no explanatory
power with respect to how humans solve/face the same problems.
In this sense they are not cognitive ! (e.g. despite IBM claims) 15
16
They are NOT Cognitive Systems
Extended version of 2D Space of Cognitive Modelling (from Vernon, 2014)
Non HUMAN ERRORS
Toronto???
https://youtu.be/C5Xnxjq63Zg
Ex. IBM Watson:
Topic: US Cities
19
20
A MORE CONSTRAINED
DESIGN APPROACH
Structuralism
• Strong equivalence between cognitive
processes and AI procedures (Cordeschi 2002,
Milkowski, 2013).
• Focus not only on the functional organization
of the processes but also on the human-
likeliness of a model (bio/psychologically
plausibility).
22
Wiener’s “Paradox”
“The best material model of a cat is another or possibly the same cat” (Rosenblueth &
Wiener45)
Z.Pylyshyn (’79): “if we do not formulate any restriction about a model we obtain the functionalism of a
Turing machine. If we apply all the possible restrictions we reproduce a whole human being”
- Also for complete simulation of complete models (e.g. very simple organisms like
the Caenorhabditis elegans, Kitano et al. 98) it is problematic a full understanding and
testing of biological hypotheses.
A Design Problem
• Need for looking at a descriptive level on which to enforce
the constraints in order to carry out a human-like
computation.
• A design perspective: between the explanatory level of
functionalism (based on the macroscopic stimulus-response
relationship) and the mycroscopic one of fully structured
models (reductionist materialism) we have, in the middle, a
lot of possible structural models.
24
“Cognition” as Design Constraint
• We need a function-structure coupling for the design of
cognitive artificial systems.
• The interpretations of the experimental results coming
from cognitive psychology/neuroscience indicate us the
algorithm procedures (the heuristics/design constraints)
that we can implement in our system in a functional-
structural way.
• I.e. the implementation can be done in different ways
(multiple realizability account of the functionalism) but
the model needs to be constrained to the target system
(needs to be structurally valid). 25
Many Structural Models
Both the presented AI approaches may build structural
models of cognition at different levels of details (having an
empirical adequacy).
26
Cognitive Function
(NL Understanding)
Cognitive Processes Neural Structures
Sintax
Morphology
Lexical
Processing…
Bio-Physical Plausibility
of the Processes
Cognitive Plausibility
of the Processes
Cognitivism Emergent AI
First take home messages
• Cognitive Artificial Models have an explanatory power only if
they are structurally valid models (realizable in different
ways and empirically adequate).
• Neural models are not necessarily more plausible than hybrid
or symbolic models (their explanatory role depends on the
way in which structural constraints are enforced on them)
• Cognitive Artificial Systems built with this design perspective
can be used as “computational experiment” and provide
results useful for refining of rethinking theoretical aspects of
the natural inspiring system.
Notable examples
(some of them)
29
GPS (General Problem Solver)
A system able to demonstrate simple logic theorems whose decision strategies were
explicitly inspired by human verbal protocols (Simon, Shaw, Newell, 1959).
Idea: the computer system had to approximate the decision operations described by the
humans in their verbal descriptions as closely as possible.
In particular, the GPS system was able to implement a key mechanism in human problem
solving: the so called means-ends analysis (or M-E heuristic). In M-E analysis the problem
solver compares the current situation with the goal situation and reduces the difference
(HEURISTIC SEARCH).
30
Nobel Prize
“bounded rationality"
Semantic Networks
Ross Quillian (1968) developed a a psychologically plausible model of human semantic
memory implemented in a computer system. The idea of Quillian was that human memory
was associative in nature and that concepts were represented as sort of nodes in graphs and
activated through a mechanism of “spreading activation” allowing to propagate information
through the network to determine relationships between objects.
31
RM Model of Past-Tense Acquisition
• Shows how emergentist approach can explain some features of
language acquisition without any predefined rule
• Training of the network corresponds to the development of mental
skills in humans. 32
Cognitive Architectures
33
Allen Newell (1990)
Unified Theory of Cognition
A cognitive architecture (Newell, 1990) implements the
invariant structure of the cognitive system.
The work on such systems started in the ‘80s (SOAR
(Newell, Laird and Rosenbloom, 1982)
It captures the underlying commonality between different
intelligent agents and provides a framework from which
intelligent behavior arises.
The architectural approach emphasizes the role of
memory in the cognitive process.
DUAL PECCS: DUAL- Prototype and Exemplars
Conceptual Categorization System
Lieto, Radicioni, Rho (IJCAI 2015, JETAI 2017)
35
36
1) Multiple representations for the same concept
2) On such diverse, but connected, representation are executed
different types of reasoning (System 1/ System 2) to integrate.
2 Cognitive Assumptions
Type 1 Processes Type 2 Processes
Automatic Controllable
Parallel, Fast Sequential, Slow
Pragmatic/contextualized
…
Logical/Abstract
…
Heterogeneous Proxytypes Hypothesis
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on
non-monotonic formalisms.
TIPICALITY
The diverse types of connected representations can coexist and point to
the same conceptual entity. Each representation can be activated as a proxy
(for the entire concept) from the long term memory to the working memory of
a cognitive agent.
(Lieto, A. A Computational Framework for Concept Representation in Cognitive Systems and
Architectures: Concepts as Heterogeneous Proxytypes, Proc. of BICA 2014)
CLASSICAL
Ex. Heterogeneous Proxytypes at work
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on
non-monotonic formalisms.
(Lieto, A. A Computational Framework for Concept Representation in Cognitive Systems and
Architectures: Concepts as Heterogeneous Proxytypes, Proc. of BICA 2014)
39
Co-referring representational Structures via Wordnet
Lieto, A., Radicioni, D., Rho, V, (2017). Dual PECCS: a cognitive system for conceptual representation
and categorization, JETAI, 29 (2), 433-452, Taylor and Francis.
S1/S2 Categorization Algorithms
40
Overview
NL Description
-The big fish eating plankton
Typical
Representations
IE step and
mapping
List of Concepts :
-Whale 0.1
-Shark 0.5
-…
Output S1
(Prototype or
Exemplar)
Check on S2
Ontological Repr.
-Whale NOT Fish
-Whale Shark OK
Output S2 (CYC)
Output S1 + S2
Whale
Whale Shark
ACT-R, SOAR, CLARION and LIDA Extended Declarative Memories with
DUAL-PECCS
Salvucci et al. 2014 (DbPedia)
Lieto A., Lebiere C., Oltramari A. (2018), The Knowledge Level in Cognitive Architectures: Current Limitations and Possibile
Developments. Cognitive Systems Research (48): 39-55, Elsevier.
DEMO https://www.youtube.com/watch?v=1KtnAWyxj-8
43
http://dualpeccs.di.unito.it
Evaluation
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic
formalisms.
Query on commonsense questions
Gold standard: for each description recorded the human answers
for the categorization task.
Stimulus Expected
Concept
Expected Proxy-
Representation
Type of Proxy-
Representation
… … … …
The primate
with red nose
Monkey Mandrill EX
The feline with
black fur that
hunts mice
Cat Black cat EX
The big feline
with yellow fur
Tiger Prototypical
Tiger
PR
46
- Concept Categorization Accuracy
- Proxyfication Accuracy
Evaluation Accuracy
Lieto, A., Radicioni, D., Rho, V, (2017). Dual PECCS: a cognitive system for conceptual
representation and categorization, JETAI, 29 (2), 433-452, Taylor and Francis.
Analysis
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic
formalisms.
- The comparison of the obtained results with human
categorization is encouraging 77-89% (results of other
AI systems for such reasoning tasks are by far lower).
- The analysis of the results revealed that it is not true
that exemplars (if similar enough to the stimulus to
categorise) are always preferred w.r.t. the
prototypes.
- Need of a more fine-grained theory explaining more in
the details the interaction bewteen co-existing
representations in the heterogeneous hypothesis.
Upshots
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic
formalisms.
Cognitively inspired systems have played and play an
important role in AI research.
In general, simple tasks for humans are the most
complicated ones for AI systems (in such problems the
cognitive heuristic approach can play an important role for
the development of better AI systems)
Notable areas: few/one shot learning, commonsense
reasoning, transfer learning, computation creativity,
narrative understanding, heuristic integration of
planning, action and goal-oriented reasoning,
computational models of emotion, analogy and methaphor
based reasoning, cognitive and social robotics,
explainable AI..(many others).
Cognitive Design for Artificial Minds
49
Forthcoming in 2021 !!
Taylor and Francis

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Cognitive Paradigm in AI Research

  • 1. The Cognitive Paradigm in the Artificial Intelligence Research Antonio Lieto Università di Torino, Dipartimento di Informatica, IT ICAR-CNR, Palermo, IT June 10, 2020, Kyiv-Mohyla National University https://www.antoniolieto.net
  • 2. Driving Questions - What characterize cognitively inspired AI systems? - What are examples of cognitively inspired AI systems? - How do they differ from standard AI systems? - How can cognitively inspired AI systems be used? 2
  • 3. From Human to Artificial Cognition 3 Inspiration
  • 4. From Human to Artificial Cognition 4 Inspiration Why? Humans (and/or other natural systems) are still, by far, the best unmatched systems able to solve a wide-range of problems
  • 5. From Human to Artificial Cognition (and back) 5 Inspiration Explanation
  • 6. “Natural/Cognitive” Inspiration and AI Early AI Cognitive or Biological Inspiration for the Design of “Intelligent Systems” M. Minsky R. Shank Modern AI “Intelligence” in terms of optimality of a performance (narrow tasks) mid‘80s A. Newell H. Simon D. Rumhelart J. McClelland N. Wiener Nowadays: Renewed attention “The gap between natural and artificial systems is still enormous” (A. Sloman, AIC 2014).
  • 7. 7 Cognitivism Nouvelle AI Focus on high level cognitive functions Mainly focused on perception Assuming structured representations (physical symbol system, Simon and Newell, 1976) Assuming unstructured representation (e.g. such as neural networks etc.) and also integration with symbolic approaches. Architectural Perspective (integration and interaction of all cognitive functions) System perspective (not necessary to consider a whole architectural perspective). Inspiration from human cognition (heuristic-driven approach) Bio-inspired computing, bottom-up approach (for learning etc.). 2 Main Perspectives
  • 8. 10 ‘Cognition is a type of computation’ Intelligence as “symbolic manipulation” Cognitivism & Symbolic Representations High level of abstraction
  • 9. Nouvelle AI and Connectionism - Sub-symbolic representations (including deep nets) —-> LEARNING, PERCEPTION, CATEGORIZATION. Low-level of abstraction
  • 10. Modern successful AI systems 10 IBM Watson (symbolic) Alpha Go (Deep Mind) (connectionist)
  • 11. A Matter of Levels Both the “cognitivist” and “nouvelle” approach can realize, in principio, “cognitive artificial systems” or “artificial models of cognition” provided that their models operate at the “right” level of description. When a biologically/cognitively inspired computational system/ architecture has an explanatory power w.r.t. the natural system taken as source of inspiration ? Which are the requirements to consider in order to design a computational model of cognition with an explanatory power? Functionalist vs Structuralist Models 11
  • 12. Functionalism • Functionalism (introduced by H. Putnam) postulates a weak equivalence between cognitive processes and AI procedures. • AI procedures have the functional role (“function as”) of human cognitive procedures. • Equivalence on the functional macroscopic properties of a given intelligent behaviour (based on the same input-output specification). • Multiple realizability (cognitive functions can be implemented in different ways). • This should produce predictive models (given an input and a set of procedures functionally equivalent to what is performed by cognitive processes then one can predict a given output). 12
  • 13. Problems with Functionalism • If the equivalence is so weak it is not possible to interpret the results of a system (e.g. interpretation of the system failures…). • A pure functionalist model (posed without structural constraints) is a black box where a predictive model with the same output of a cognitive process can be obtained with no explanatory power. 13
  • 14. Birds and Jets - Both Birds and Jets can fly but a jet is not a good explanatory model of a bird since its flights mechanisms are different from the mechanism of bird. - Purely functional models/systems are not “computational models of cognition” (they have no explanatory power w.r.t. the natural system taken as source of inspiration). 14
  • 15. Modern “Functional” Systems in AI They are very good artificial systems but they have no explanatory power with respect to how humans solve/face the same problems. In this sense they are not cognitive ! (e.g. despite IBM claims) 15
  • 16. 16 They are NOT Cognitive Systems Extended version of 2D Space of Cognitive Modelling (from Vernon, 2014)
  • 19. 19
  • 20. 20
  • 22. Structuralism • Strong equivalence between cognitive processes and AI procedures (Cordeschi 2002, Milkowski, 2013). • Focus not only on the functional organization of the processes but also on the human- likeliness of a model (bio/psychologically plausibility). 22
  • 23. Wiener’s “Paradox” “The best material model of a cat is another or possibly the same cat” (Rosenblueth & Wiener45) Z.Pylyshyn (’79): “if we do not formulate any restriction about a model we obtain the functionalism of a Turing machine. If we apply all the possible restrictions we reproduce a whole human being” - Also for complete simulation of complete models (e.g. very simple organisms like the Caenorhabditis elegans, Kitano et al. 98) it is problematic a full understanding and testing of biological hypotheses.
  • 24. A Design Problem • Need for looking at a descriptive level on which to enforce the constraints in order to carry out a human-like computation. • A design perspective: between the explanatory level of functionalism (based on the macroscopic stimulus-response relationship) and the mycroscopic one of fully structured models (reductionist materialism) we have, in the middle, a lot of possible structural models. 24
  • 25. “Cognition” as Design Constraint • We need a function-structure coupling for the design of cognitive artificial systems. • The interpretations of the experimental results coming from cognitive psychology/neuroscience indicate us the algorithm procedures (the heuristics/design constraints) that we can implement in our system in a functional- structural way. • I.e. the implementation can be done in different ways (multiple realizability account of the functionalism) but the model needs to be constrained to the target system (needs to be structurally valid). 25
  • 26. Many Structural Models Both the presented AI approaches may build structural models of cognition at different levels of details (having an empirical adequacy). 26 Cognitive Function (NL Understanding) Cognitive Processes Neural Structures Sintax Morphology Lexical Processing… Bio-Physical Plausibility of the Processes Cognitive Plausibility of the Processes Cognitivism Emergent AI
  • 27. First take home messages • Cognitive Artificial Models have an explanatory power only if they are structurally valid models (realizable in different ways and empirically adequate). • Neural models are not necessarily more plausible than hybrid or symbolic models (their explanatory role depends on the way in which structural constraints are enforced on them) • Cognitive Artificial Systems built with this design perspective can be used as “computational experiment” and provide results useful for refining of rethinking theoretical aspects of the natural inspiring system.
  • 29. 29
  • 30. GPS (General Problem Solver) A system able to demonstrate simple logic theorems whose decision strategies were explicitly inspired by human verbal protocols (Simon, Shaw, Newell, 1959). Idea: the computer system had to approximate the decision operations described by the humans in their verbal descriptions as closely as possible. In particular, the GPS system was able to implement a key mechanism in human problem solving: the so called means-ends analysis (or M-E heuristic). In M-E analysis the problem solver compares the current situation with the goal situation and reduces the difference (HEURISTIC SEARCH). 30 Nobel Prize “bounded rationality"
  • 31. Semantic Networks Ross Quillian (1968) developed a a psychologically plausible model of human semantic memory implemented in a computer system. The idea of Quillian was that human memory was associative in nature and that concepts were represented as sort of nodes in graphs and activated through a mechanism of “spreading activation” allowing to propagate information through the network to determine relationships between objects. 31
  • 32. RM Model of Past-Tense Acquisition • Shows how emergentist approach can explain some features of language acquisition without any predefined rule • Training of the network corresponds to the development of mental skills in humans. 32
  • 33. Cognitive Architectures 33 Allen Newell (1990) Unified Theory of Cognition A cognitive architecture (Newell, 1990) implements the invariant structure of the cognitive system. The work on such systems started in the ‘80s (SOAR (Newell, Laird and Rosenbloom, 1982) It captures the underlying commonality between different intelligent agents and provides a framework from which intelligent behavior arises. The architectural approach emphasizes the role of memory in the cognitive process.
  • 34. DUAL PECCS: DUAL- Prototype and Exemplars Conceptual Categorization System Lieto, Radicioni, Rho (IJCAI 2015, JETAI 2017)
  • 35. 35
  • 36. 36 1) Multiple representations for the same concept 2) On such diverse, but connected, representation are executed different types of reasoning (System 1/ System 2) to integrate. 2 Cognitive Assumptions Type 1 Processes Type 2 Processes Automatic Controllable Parallel, Fast Sequential, Slow Pragmatic/contextualized … Logical/Abstract …
  • 37. Heterogeneous Proxytypes Hypothesis The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic formalisms. TIPICALITY The diverse types of connected representations can coexist and point to the same conceptual entity. Each representation can be activated as a proxy (for the entire concept) from the long term memory to the working memory of a cognitive agent. (Lieto, A. A Computational Framework for Concept Representation in Cognitive Systems and Architectures: Concepts as Heterogeneous Proxytypes, Proc. of BICA 2014) CLASSICAL
  • 38. Ex. Heterogeneous Proxytypes at work The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic formalisms. (Lieto, A. A Computational Framework for Concept Representation in Cognitive Systems and Architectures: Concepts as Heterogeneous Proxytypes, Proc. of BICA 2014)
  • 39. 39 Co-referring representational Structures via Wordnet Lieto, A., Radicioni, D., Rho, V, (2017). Dual PECCS: a cognitive system for conceptual representation and categorization, JETAI, 29 (2), 433-452, Taylor and Francis.
  • 41. Overview NL Description -The big fish eating plankton Typical Representations IE step and mapping List of Concepts : -Whale 0.1 -Shark 0.5 -… Output S1 (Prototype or Exemplar) Check on S2 Ontological Repr. -Whale NOT Fish -Whale Shark OK Output S2 (CYC) Output S1 + S2 Whale Whale Shark
  • 42. ACT-R, SOAR, CLARION and LIDA Extended Declarative Memories with DUAL-PECCS Salvucci et al. 2014 (DbPedia) Lieto A., Lebiere C., Oltramari A. (2018), The Knowledge Level in Cognitive Architectures: Current Limitations and Possibile Developments. Cognitive Systems Research (48): 39-55, Elsevier.
  • 45. Evaluation The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic formalisms. Query on commonsense questions Gold standard: for each description recorded the human answers for the categorization task. Stimulus Expected Concept Expected Proxy- Representation Type of Proxy- Representation … … … … The primate with red nose Monkey Mandrill EX The feline with black fur that hunts mice Cat Black cat EX The big feline with yellow fur Tiger Prototypical Tiger PR
  • 46. 46 - Concept Categorization Accuracy - Proxyfication Accuracy Evaluation Accuracy Lieto, A., Radicioni, D., Rho, V, (2017). Dual PECCS: a cognitive system for conceptual representation and categorization, JETAI, 29 (2), 433-452, Taylor and Francis.
  • 47. Analysis The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic formalisms. - The comparison of the obtained results with human categorization is encouraging 77-89% (results of other AI systems for such reasoning tasks are by far lower). - The analysis of the results revealed that it is not true that exemplars (if similar enough to the stimulus to categorise) are always preferred w.r.t. the prototypes. - Need of a more fine-grained theory explaining more in the details the interaction bewteen co-existing representations in the heterogeneous hypothesis.
  • 48. Upshots The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based on non-monotonic formalisms. Cognitively inspired systems have played and play an important role in AI research. In general, simple tasks for humans are the most complicated ones for AI systems (in such problems the cognitive heuristic approach can play an important role for the development of better AI systems) Notable areas: few/one shot learning, commonsense reasoning, transfer learning, computation creativity, narrative understanding, heuristic integration of planning, action and goal-oriented reasoning, computational models of emotion, analogy and methaphor based reasoning, cognitive and social robotics, explainable AI..(many others).
  • 49. Cognitive Design for Artificial Minds 49 Forthcoming in 2021 !! Taylor and Francis