Towards which Intelligence? Cognition as Design Key for
building Artificial Intelligent Systems
Antonio Lieto
University of Turin, Dipartimento di Informatica, Italy
ICAR - CNR, Palermo, Italy
Symposium “Bridging the Gap between Cognitive Psychology and Artificial Intelligence”
ESCOP 2015, Paphos, Cyprus, 19th September 2015

Outline
• Which “Intelligence” in Artificial
Intelligence (AI)?
• Cognitive AI: methodological and
technical considerations.
• A case study (Time permitting !): System
dealing with Common Sense Reasoning
(Conceptual Categorization, paper
presented at IJCAI 2015). 2

Which“Intelligence” in AI? (partial overview)
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)
J. McCarthy
mid‘80s
A.
Newell
H. Simon
deep learning

A focus shift in AI
P. Langley (2012) “Vision the early days of AI: Understanding and
reproducing, in computational systems, the full range of intelligent
behavior observed in humans”.
Why this view has been abandoned?
- Emphasis on quantitative results and metrics of performance:
(“machine intelligence”: achieving results and optimize them !)
- Commercial success of narrow applications etc.
Nowadays it is regaining attention since “The gap between natural
and artificial systems is still enormous” (A. Sloman, AIC 2014)
4

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

2 Perspectives
The “Cognitive Systems” one (Brachman and Lemnios,
2002), referring to the discipline that: “designs,
constructs, and studies computational artifacts that
exhibit the full range of human intelligence”.
The“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).
6

Cognitive Systems Paradigm
Langley (2012):
• Focus on high level human cognitive functions
• Assuming structured representations (physical symbol
system, Simon and Newell, 1976)
• Architectural Perspective (integration and interaction
of all cognitive functions)
• Inspiration from human cognition

“Nouvelle” AI paradigm
• Focus not only on high level cognitive functions (but
also on perception, vision etc.)
• Assuming unstructured representation (e.g. such as
neural networks etc.) and also integration with
symbolic approaches.
• System perspective (not necessary to consider a
whole architectural perspective).
• Bio-inspired computing, bottom-up approach (for
learning etc.). 8

A Matter of Levels
• Both the “classical” and “nouvelle” approach can
realize, in principle, “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 9

Functionalism
• Functionalism (introduced by Putnam) postulates a weak equivalence
between cognitive processes and AI procedures.
• AI procedures have the functional role (“function as”) human cognitive
procedures.
• Multiple realizability (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). 10

Problem 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.
• An analogy: 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 (like IBM Watson) are not
“computational models of cognition” 11

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-psychological plausibility).
• Wiener’s paradox: “The best material model of a
cat is another or possibly the same cat”
12

A Design Problem
Pylyshyn (1979): “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. 13

“Cognition” as Design Constraint
• We need a function-structure coupling for the design
of cognitive artificial system.
• The interpretations of the experimental results
coming from cognitive psychology 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). 14

Case Study
A Common Sense Reasoning System
IJCAI’15 Paper
Antonio Lieto, Daniele P. Radicioni and Valentina Rho, “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.
15

System Assumptions
– Representation:
– conceptual structures as heterogeneous proxytypes (Lieto 2014);
compliance with the computational frameworks of cognitive
architectures (General Architectures for Intelligence).
– Reasoning:
– 2 types of common sense inference (based on prototypes and
exemplars).
– Dual process reasoning (Common sense + Standard
categorization).
– Integration into the ACT-R cognitive architecture (Anderson et al.
2004).

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

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

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).

System Conceptual Structure
20
differ-
spec-
terms
illian,
es can
egion
spec-
an, on
f con-
ilarly,
mbolic
ell as
an be
tems,
artic-
so for
iple–,
is-a: feline
color: yellow
hasPart: fur
hasPart: tail
hasPart: stripes
...
conceptual space
representation
concept Tiger
Kingdom: Animalia
Class: Mammalia
Order: Carnivora
Genus: Panthera
Species: P. tigris
prototype of Tiger exemplars of Tiger
white-tiger
is-a: feline
color: white
hasPart: fur
hasPart: tail
hasPart: stripes
...
...
ontological
representation
classical information
Typicality-based
knowledge
Classical
knowledge
Hybrid Knowledge Base

Dual Process Reasoning
11
Reasoning Harmonization based on the dual process
(Stanovitch and West, 2000; Kahnemann 2011).
In human cognition, type 1 processes are executed fast and
are not based on logical rules. Then they are checked against
more logical deliberative processes (type 2 processes).
Type 1 Processes Type 2 Processes
Automatic Controllable
Parallel, Fast Sequential, Slow
Pragmatic/contextualized Logical/Abstract

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

Cognitive Architectures
23
Allen Newell (1990)
Unified Theory of Cognition
A cognitive architecture (Newell, 1990) implements the
invariant structure of the cognitive system.
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.

Heterogeneous Proxytypes
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.
Concepts as Heterogeneous Proxytypes
(Lieto, 2014).
Stored in our long term memory and,
activate in our working memory only
specific representations (“go proxy”).

Evaluation (Human-Machine Comparison)
• 122 textual stimuli created by a team of neuropsychologists and
tested in categorisation task during neuromaging experiments
(FMRI).
• 40 human subjects were requested to categorize such stimuli
(and to specify the corresponding representation activated).
• The provided answers are a Gold Standard and represents the
expected results.
• 84% of system answers overlapping with human responses.
The computational experimentation also provided insights for the
refinement of the theory (details in the paper!)

Take home message
• After two decades, Cognitive AI field is gaining a
renewed attention.
• Cognitive Artificial Models of AI are proxyies of a
target system and have an explanatory power only if
they are structurally valid models (realizable in
different ways).
• Cognitive Artificial Systems built with this design
perspective can have an explanatory role for the
theory they implement and the “computational
experiment” can provide results useful for refining of
retaining theoretical aspects.

Join us at AIC 2015 in Turin ! 28-29 Sept. 2015
27

Thanks !
Contacts:
lieto.antonio@gmail.com
Symposium “Bridging the Gap between Cognitive Psychology and Artificial Intelligence”
ESCOP 2015, Paphos, Cyprus, 19th September 2015