Invited talk at the First International BICA School, Moscow

1. Computational Explanation in BIC(A/S)
Antonio Lieto
University of Turin, Dipartimento di Informatica, Italy
ICAR - CNR, Palermo, Italy
http://www.di.unito.it/~lieto/
Fierces on BICA, International School on Biologically Inspired Cognitive Architectures
Moscow, Russia, 21-24 April 2016

2. From Human to Artificial Cognition
(and back)
2
Inspiration
Explanation
Lieto and Radicioni, Cognitive Systems Research, 2016

3. Research Questions
When a biologically 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 ?
3

4. Outline
- Cognitive AI Paradigms: some
methodological and technical considerations.
- Functionalist vs Structuralist Approach.
- Case study on Knowledge Level in
Cognitive Architectures.
4

5. Cognitive AI
Attention to the heuristics-based solutions
adopted by humans (e.g. Gigerenzer & Todd,
1999) for combinatorial problems (“bounded
rationality heuristics”).
Heuristics realize/implement some cognitive
functions and are responsible of the macroscopic
external behaviour of an agent.
5

6. “Natural/Cognitive” Inspiration and AI
Early AI
Cognitive 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
e.g. IBM Watson…
N. Wiener

7. A focus shift in AI
Vision the early days of AI: “Understanding and reproducing, in
computational systems, the full range of intelligent behavior
observed in humans” (P. Langley, 2012).
This view was abandoned. Why?
- Emphasis on quantitative results and metrics of performance:
(“machine intelligence”: achieving results and optimize
them !)
- Renewed attention since “The gap between natural and
artificial systems is still enormous” (A. Sloman, AIC 2014).
7

8. 2 Main Perspectives
“Cognitive Systems” (Brachman and Lemnios, 2002):
“designs, constructs, and studies computational artifacts that
exhibit the full range of human intelligence”. [Cognitivist
approach, Vernon 2014].
“Nouvelle AI” (e.g. Parallel Distributed Processing
(Rumhelarth and McLelland, 1986) based on bio-plausibility
modelling techniques allowing the functional reproduction of
heuristics in artificial systems (neglecting the physical and
chemical details). [Emergent approach, Vernon 2014].
8

9. Type 1/Type 2 features
9
Cognitivism Nouvelle AI
Focus on high level cognitive functions Main focus only 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.).

10. A Matter of Levels
• Both the “classical” and “novuelle” approach can
realize, in principio, “cognitive artificial
systems” or “artificial models of cognition”
provided that their models operate at the “right”
level of description.
• A debated problem in AI and Cognitive Science
regards the legitimate level of descriptions of
such models (and therefore their explanatory
power).
Functionalist vs Structuralist Models 10

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”) human
cognitive procedures.
• Multiple realizability (cognitive functions can be implemented in
different ways).
• Equivalence on the functional macroscopic properties of a given
intelligent behaviour (based on the same input-output specification).
• 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). 11

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 failure…).
• 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.
12

13. Birds and Jets
- Both a Bird and a Jet 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).
13

14. Structuralism
• Strong equivalence between cognitive processes
and AI procedures (Milkowski, 2013).
• Focus not only on the functional organization of
the processes but also on the human-likeliness
of a model (bio-psychologically plausibility).
14

15. Wiener’s “Paradox”
“The best material model of a cat is another or possibly the
same cat”
- Difficulty of realizing models of a given natural system.
- Need of proxy-models (i.e. good approximations)
15

16. A Design Problem
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”
• 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. 16

17. Many Structural Models
Both the presented AI approaches may build structural
models of cognition at different levels of details (having an
empirical adequacy => Paul Verschure’s yesterday talk).
17
Cognitive Function
(NL Understanding)
Cognitive Processes Neural Structures
Sintax
Morphology
Lexical
Processing…
Biological Plausibility of
Processes
Cognitive Plausibility
of the Processes
1:N 1:N

18. Many Structural Models
Both the presented AI approaches may build structural
models of cognition at different levels of details (having an
empirical adequacy => Paul Verschure’s yesterday talk).
18
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

19. Take home message (part 1)
• Cognitive Artificial Models (BICA) have an
explanatory power only if they are structurally
valid models (realizable in different ways and
empirically adequate).
• Cognitive Artificial Systems built with this design
perspective have an explanatory role for the
theory they implement and the “computational
experiment” can provide results useful for refining
of rethinking theoretical aspects of the natural
inspiring system.
Lieto, under review

20. Case Study: Knowledge in Humans and
CAs
• Knowledge in Humans
• Knowledge Representations in some current
Cognitive Architectures (CLARION, LIDA, ACT-R)
20

21. In Cognitive Science there were/are different
contrasting theories about “how humans represent
and organize the information in their mind”…This
research also influenced Artificial Intelligence
21

22. Classical Theory – Ex.
22
TRIANGLE = Polygon with 3 corners and sides

23. Prototype Theory
23
Prototype Theory (Rosh
E., 1975)
Category membership is not based on
necessary and sufficient conditions but on
typicality traits.
There are members of a category that are more
typical and cognitively relevant w.r.t. others.
Ex: BIRD, {Robin, Toucan, Penguin…}

24. Prototypes and Prototypical Reasoning
• Categories based on prototypes (Rosh,1975)
• New items are compared to the prototype
atypical
typical
P

25. Exemplars and Exemplar-based Reasoning
• Categories as composed by a list of exemplars. New
percepts are compared to known exemplars (not to
Prototypes).

26. Heterogeneous 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.
Different representational structures have different accessing procedures
(reasoning) to their content.
Prototypes, Exemplars and other conceptual representations can co-exists
and be activated in different contexts (Malt 1989).

27. Representations and Cognitive
Mechanisms
– Conceptual structures as heterogeneous
proxytypes (Lieto 2014).
A proxytype is any element of a complex
representational network stored in long-term
memory corresponding to a particular category
that could be tokened in working memory to “go
proxy” for that category (Prinz, 2002) => inspired
by Barsalou (1999)

28. Ex. Heterogeneous Proxytypes at work
29
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.
from Lieto 2014, BICA

29. Conceptual Heterogeneity and CA
– How current CAs deal with Conceptual
Heterogeneity ?
– Analysis of ACT-R, CLARION and LIDA
Knowledge Level
– Some insights and suggestions

30. Type 1/Type 2 features
30
ACT-R (Anderson et
al. 2004)
CLARION (Sun, 2006) Vector-LIDA
(Franklin et al.
2014)
Concepts as chunks
(symbolic structures)
Neural networks + Symbol
Like representations
High dimensional vector
spaces
Assuming structured
representations (physical
symbol system, Simon and
Newell, 1976)
Assuming a dual
representations
Vectors treated as symbol-
like representations (e.g.
compositionally via vector
blending)
Sub-symbolic and Bayesian
activation of chunks
Subsymbolic activation of
conceptual chunks
Similarity based vectorial
activation
Prototypes and Exemplars
models of categorisation
available in separation
(extended in Lieto et al.
IJCAI 2015)
Prototypes and Exemplars
models of categorisation
NOT available (proposed in
Lieto et al. submitted to
JETAI)
Prototypes and Exemplars
models of categorisation
NOT available (current
work)

31. Upshots
– There are structural differences (at the
process level) between the analysed
architectures in dealing with a plausible
model of human conceptual
representation and reasoning.
– All of these architectures can in principle
account with these constraints but ACT-R
has currently a better explanatory model of
the human representational and reasoning
conceptual structures.

32. Computational Explanation in BICA
Antonio Lieto
University of Turin, Dipartimento di Informatica, Italy
ICAR - CNR, Palermo, Italy
http://www.di.unito.it/~lieto/
Fierces on BICA, International School on Biologically Inspired Cognitive Architectures
Moscow, Russia, 21-24 April 2016

33. References
Gigerenzer, G., & Todd, P. M. (1999). Simple heuristics that make us smart. Oxford University
Press, USA.
Langley, P. (2012). The cognitive systems paradigm. Advances in Cognitive Systems, 1, 3–13.
Lieto, A. "A Computational Framework for Concept Representation in Cognitive Systems and
Architectures: Concepts as Heterogeneous Proxytypes" in Proceedings of BICA 2014, 5th Int.
Conference of Biologically Inspired Cognitive Architectures, Boston, Massachusetts Institute of
Technology (MIT), USA, 7-9 November 2014. Procedia Computer Science, Vol. 41 (2014), pp. 6-14
Lieto, A, Radicioni D.P. "From Human to Artificial Cognition and Back: New Perspectives on
Cognitively Inspired AI Systems", in Cognitive Systems Research, 39, 1-3 (2016), Elsevier
Lieto, A., Daniele P. Radicioni D.P. and Rho, V. A Common-Sense Conceptual Categorization
System Integrating Heterogeneous Proxytypes and the Dual Process of Reasoning". In Proceedings
of the International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, July 2015, pp.
875-881. AAAI press.
Milkowski, M. (2013). Explaining the computational mind. Mit Press.
Newell, A., & Simon, H. A. (1972). Human problem solving volume 104. Prentice-Hall Englewood.
Putnam, H.: Minds and machines. In: Hook, S. (ed.) Dimensions of Mind, pp. 138–164. Macmillan
Publishers, London (1960)
Pylyshyn, Z.W.: Complexity and the study of artificial and human intelligence. In: Ringle, M. (ed.)
Philosophical Perspectives in Artificial Intelligence, Harvester, Brighton (1979)
Rosenblueth, A., Wiener, N.: The role of Models in Sciences. Phil. Sci. 12, 316–321 (1945).
Vernon, D. (2014). Artificial cognitive systems: A primer. MIT Press.