=Paper=
{{Paper
|id=Vol-2776/paper-5
|storemode=property
|title=What Cognitive Research Can Do for AI: a Case Study (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2776/paper-5.pdf
|volume=Vol-2776
|authors=Antonio Lieto,Gian Luca Pozzato
|dblpUrl=https://dblp.org/rec/conf/aiia/LietoP20
}}
==What Cognitive Research Can Do for AI: a Case Study (short paper)==
https://ceur-ws.org/Vol-2776/paper-5.pdf
What cognitive research can do for AI: a case
study
Antonio Lietoa,b , Gian Luca Pozzatoa
a Università di Torino, Dipartimento di Informatica, Italy, c.so Svizzera 185, 10149 Turin, Italy
b ICAR, National Research Council of Italy, Via Ugo La Malfa 153, 90146 Palermo, Italy
Abstract
This paper presents a practical case study showing how, despite the nowadays limited collaboration
between AI and Cognitive Science (CogSci), cognitive research can still have an important role in the
development of novel AI technologies. After a brief historical introduction about the reasons of the divorce
between AI and CogSci research agendas (happened in the mid’80s of the last century), we try to provide
evidence of a renewed collaboration by showing a recent case study on a commonsense reasoning system,
built by using insights from cognitive semantics.
1. Introduction
The research in Artificial Intelligence has been based, from a historical standpoint, on a strong
collaboration between computer scientists, psychologists, engineers, philosophers and biologists
working in the Cognitive Science field. This collaboration, fostered by the influence of the
cybernetic approach to the study of natural and artificial systems, has produced – along the years
– the development of fruitful research lines in bionics, robotics, biologically and neurally inspired
systems and, more in general, in the area of cognitive artificial systems and systems science
[1][2].
After decades of mutual and pioneering collaborations, however, Artificial Intelligence and
Cognitive Science have produced several sub-disciplines, each one with its own goals, methods
and criteria for evaluation. This fragmentation, on the one hand, has facilitated the development
of some AI systems able to produce super-human competences, in restricted domains (such as
in computer vision, or in games such as chess, Jeopardy, Go, etc.). On the other hand, however,
it has been based on a divide et impera approach that has significantly inhibited the cross-field
collaborations and the scientific efforts aimed at investigating a more general picture of what
natural and artificial intelligence are, and how intelligent artefacts can be designed by taking into
account the insights coming from the natural world. In more recent years, however, the area of
cognitively inspired artificial systems has attracted a renewed attention both from academia and
industry and the awareness about the need for additional research in this interdisciplinary field
has gained widespread acceptance. To use the words by Aaron Sloman, in fact, “the gap between
AIxIA 2020 Discussion Papers Workshop
" antonio.lieto@unito.it (A. Lieto); gianluca.pozzato@unito.it (G.L. Pozzato)
~ https://www.antoniolieto.net (A. Lieto); http://www.di.unito.it/pozzato/ (G.L. Pozzato)
0000-0002-8323-8764 (A. Lieto); 0000-0002-3952-4624 (G.L. Pozzato)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
natural and artificial intelligence is still enormous” [3] and the research in this area seems now
crucial for the development of better artificial systems.
In particular, cognitive research can provide useful insights about a wide range of tasks that
seem to be particularly easy to do for humans (due to the automatic adoption of evolutionarily
shaped heuristics1 ) and are, on the other hand, still particularly hard to solve for artificial systems
[2]. In the following pages we detail a recent case study in the field of commonsense reasoning,
a notorious problem in AI still in search of a suitable solution. In particular, the case study
addresses a specific instance of such a problem: namely, the problem of commonsense concept
combination.
2. A case study in commonsense reasoning: the TCL logic
The generative capability of inventing novel concepts by combining the typical knowledge of
pre-existing ones is an important phenomenon in human cognition. Such ability, in fact, concerns
high-level capacities associated to creative thinking and problem solving. Still, it represents an
open challenge in the field of artificial intelligence [5]. Dealing with this problem requires, from an
AI and cognitive modelling perspective, the harmonization of two conflicting requirements that are
hardly accommodated in artificial systems: the need of a syntactic and semantic compositionality
(typical of logical systems) and that one concerning the exhibition of typicality effects [6].
According to a well-known argument [7], in fact, prototypes (i.e. commonsense conceptual
representations based on typical properties) are not compositional. The argument runs as follows:
consider a concept like PET FISH. It results from the composition of the concept pet and of
the concept fish. However, the prototype of pet fish cannot result from the composition of the
prototypes of a pet and a fish: e.g. a typical pet is furry and warm, a typical fish is grayish,
but a typical pet fish is neither furry and warm nor grayish (typically, it is red). The PET FISH
problem is a paradigmatic case for what concerns the difficulty of modelling the phenomenon
of human-like concept combination. Recently, a logical framework able to account for the PET
FISH problem has been proposed in the field of nonmonotonic Description Logics of typicality:
TCL (Typicality-based Compositional Logic, for the details see [8]). This logic combines three
main ingredients. The first one relies on the DL of typicality A L C + TR introduced in [9] which
allows to describe the prototype of a concept. In this logic, “typical” properties can be directly
specified by means of a “typicality” operator T enriching the underlying DL, and a TBox can
contain inclusions of the form T(C ) ⊑ D to represent that “typical C s are also D s ”. As a difference
with standard DLs, in the logic A L C + TR one can consistently express exceptions and reason
about defeasible inheritance as well. For instance, a knowledge base can consistently express that
“normally, athletes are fit”, whereas “sumo wrestlers usually are not fit” by T(Athlete) ⊑ Fit and
T(SumoWrestler) ⊑ ¬Fit , given that SumoWreslter ⊑ Athlete. The semantics of T is characterized
by the properties of rational logic, recognized as the core properties of nonmonotonic reasoning.
As a second ingredient, the logic TCL exploits a distributed semantics similar to the one of
probabilistic DLs known as DISPONTE [10], allowing to label inclusions T(C ) ⊑ D with a real
1 Heuristics (or judgements heuristics) are, according to the definition from Gigerenzer [4] , shortcuts of thought
that take a minimum amount of time, knowledge and calculation (computation) to process adaptive choices in concrete
environments. This kind of shortcuts guides, on the basis of empirical rules (emerging from previous experience and
knowledge), our daily actions that must be carried out immediately orin a short time and relying on limited knowledge.
42
number between 0.5 and 1, representing its degree of belief/probability, assuming that each
axiom is independent from each others. As an example, we can formalize that we believe that
a typical athlete is fit with degree 0.9, whereas we believe that, normally, athletes are young,
but with degree 0.75, with the inclusions 0.9 :: T(Athlete) ⊑ Fit and 0.75 :: T(Athlete) ⊑ Young ,
respectively. Degrees of belief in typicality inclusions allow to define a probability distribution
over scenarios: roughly speaking, a scenario is obtained by choosing, for each typicality inclusion,
whether it is considered as true or false.
Finally, TCL employs a heuristics inspired by cognitive semantics [11] for the identification of a
dominance effect between the concepts to be combined: for every combination, we distinguish a
HEAD, representing the stronger element of the combination, and a MODIFIER. The basic idea
is: given a KB and two concepts C H (HEAD) and C M (MODIFIER) occurring in it, we consider
only some scenarios in order to define a revised knowledge base, enriched by typical properties
of the combined concept C ⊑ C H ⊓C M .
In TCL , given a hybrid KB K = 〈R, T , A 〉 (composed by typical and standard or rigid assertions,
i.e. assertion with and without exceptions, as derived from [12]) and given two concepts C H
and C M occurring in K , the logic allows defining a prototype of the compound concept C as
the combination of the HEAD C H and the MODIFIER C M , where the typical properties of the
form T(C ) ⊑ D (or, equivalently, T(C H ⊓ C M ) ⊑ D ) to ascribe to the concept C are obtained by
considering blocks of scenarios with the same probability, in decreasing order starting from the
highest one. Here all the inconsistent scenarios are discarded, then: (1) we discard those scenarios
considered as trivial, consistently inheriting all the properties from the HEAD from the starting
concepts to be combined; (2) among the remaining ones, we discard those inheriting properties
from the MODIFIER in conflict with properties that could be consistently inherited from the
HEAD; (3) if the set of scenarios of the current block is empty, i.e. all the scenarios have been
discarded either because trivial or because preferring the MODIFIER, we repeat the procedure
by considering the block of scenarios, having the immediately lower probability. Remaining
scenarios are those selected by TCL . The ultimate output is a KB in TCL whose set of typicality
properties is enriched by those of the combined concept C . Given a scenario w satisfying the
above properties, the prototype of C is defined as the set of inclusions p :: T(C ) ⊑ D , for all
T(C ) ⊑ D that are entailed from w in the logic TCL .
An important element emerging from this framework, and relevant with respect to the overall
overview proposed in this paper, lies in the fact that it would have been not possible to model
all such phenomena without considering all the three ingredients of such logic (including the
HEAD-MODIFIER heuristics). In other words, all such elements are individually necessary
but only jointly sufficient to tackle a complex problem like the one described above. This is a
symptom of the fact that, the application (and integration) of cognitive heuristics in artificial
systems and formalisms, can still play an important role in AI.
3. TCL applied to creative problem solving
A first application developed from TCL is a system able to dynamically generate novel knowledge
in the cases in which the original goal cannot be directly satisfied. The overall pipeline of the
system can be described as follows: the system receives in input a certain goal to achieve. The
goal is expressed in terms of tuples representing the desired final state. For example: a goal can be
43
expressed as {Object, Cutting, Graspable} to identify the scope of retrieving, from the inventory
of the available knowledge in the agent declarative memory, an element that is a graspable object
able to cut some surfaces. Once processed the input, the system verifies, via a searching process
in the hybrid, probabilistic, knowledge base assumed in TCL , whether there is some element that
can directly satisfy the desired conditions. If so, the element(s) (if any) satisfying the request
are returned and ranked in descending order of probability. If not, the system tries to perform,
via WordNet (https://wordnet.princeton.edu/), a task of semantic-driven goal-reformulation by
looking for synonyms and hyperonyms of the terms specified in input (in order to find at least a
minimal set of candidate concepts sharing, if considered jointly, all the required goal desiderata).
Once this process is executed, and the minimal set of candidate concepts is reached, the system
adopts the typicality-based reasoning procedure of concept combination of TCL . As an example,
suppose to have: G = {Object, Cutting, Graspable}, and suppose that the knowledge base contains
Spoon ⊑ Graspable, 0.85 :: T(Spoon) ⊑ ¬Cutting , 0.9 :: T(Vase) ⊑ Graspable, Vase ⊑ Object .
Both Vase and Spoon are included in the list of candidate concepts to be combined (along with
other concepts satisfying, for example other properties of the goal such as, for example, being
able to cut some surface). As a second step, for each item in the list of candidate concepts
to be combined, the system computes a rank of the concept as the sum of the probabilities of
the properties also belonging to the goal, assuming a score of 1 in case of a rigid property. In
the example, Vase is ranked as 0.9 + 1 = 1.9, since both Graspable and Object are properties
belonging to the goal: for the former we take the probability 0.9 of the typicality inclusion
T(Vase) ⊑ Graspable, for the latter we provide a score of 1 since the property Vase ⊑ Object is
rigid. Concerning the concept Spoon, the system computes a rank of 1: indeed, the only inclusion
matching the goal is Spoon ⊑ Graspable. Finally, the system checks whether the concept obtained
by combining the candidate concepts with the highest ranks, (e.g. C 1 and C 2 in case of only 2
concepts), is able to satisfy the initial goal. The system computes a double attempt, by considering
first C 1 as the HEAD and C 2 as the MODIFIER and, in case of failure, C 2 as the HEAD and C 1 as
the MODIFIER. We tested our system in the task of object invention via conceptual composition.
This task is considered an important proxy of natural intelligence [13] since such ability is
found, in nature, only in primates (humans and great apes) and in ravens [14]. As an example
of the obtained results: given the above mentioned goal of looking for a graspable object able
to cut, the system proposed the combination Stone ⊓ Shelf as a solution, thus suggesting a
combined concept having the characteristics resembling a rudimentary KnifeWithAWoodHandle.
The obtained results reached state of the art when compared with OROC [15] the only available
system able to perform the same task and, in addition, we also extended our evaluation to human
subjects showing a good level of performance match with human responses [16]. This result was
reinforced by the showed compliance of such a mechanisms with different cognitive architectures
like SOAR and the Common Model of Cognition, by extending, de facto, their knowledge level
capabilities [17].
4. TCL applied to content generation and suggestion
A completely different application of TCL has been exploited in DENOTER [18]: a content
generator and suggestion system exploiting the logic TCL in order to generate and suggest novel
44
editorial genres for RaiPlay (https://www.raiplay.it), the online platform of on-demand contents
of RAI (RAdio televisione Italiana, http://www.rai.it. An overview of the DENOTER pipeline is
reported in the figure 1). DENOTER is implemented in Python and it makes use of the library
owlready2 (https://pythonhosted.org/Owlready2/) for relying on the services of efficient DL
reasoners (like HermiT). DENOTER first builds a prototypical description of basic genres
available in RaiPlay, namely: action/adventure, kids, comedy, drama, science fiction, horror,
musical, religious, sentimental, and thriller.
To this aim, a web crawler extracts metadata from multimedia contents available on the
platform. More in detail, for each item (program, episode, etc.) the crawler extracts (i) the genre
to which it belongs and (ii) the set of “significant” words (i.e., excluding prepositions, proper
names, articles, etc.) occurring in the description of each item, as well as their frequency. These
information are used in order to provide a description of each basic genre in terms of its typical
properties in the logic TCL , where the frequency of a concept/word for a genre is obtained from
the number of occurrences of such a concept/word in the items belonging to that genre. The five
properties with the highest frequency over 0.5 are included in the prototypical description of each
basic genre.
DENOTER combines the two basic genres by implementing a variant of CoCoS [19], a Python
implementation of reasoning services for the logic TCL in order to exploit efficient DLs reasoners
for checking both the consistency of each generated scenario and the existence of conflicts among
properties. More in detail, DENOTER considers both the available choices for the HEAD and
the MODIFIER, and it allows to restrict its concern to a given and fixed number of inherited
properties. As an example, the new, derived genre combining kids and drama with the limit fixed
to four properties has the following TCL description (concept Kids ⊓ Drama):
0.83 :: T(Kids ⊓ Drama) ⊑ Life
0.72 :: T(Kids ⊓ Drama) ⊑ Queen
0.7 :: T(Kids ⊓ Drama) ⊑ DeadPerson
0.64 :: T(Kids ⊓ Drama) ⊑ World
Obviously, rigid properties (i.e. properties that do not exhibit any exception) of both basic
concepts Kids and Drama are inherited by the derived concept, and this avoids the system to
consider the property Homicide, even if it has the highest probability/degree of belief associated
to the prototypical description of Drama. DENOTER is also able to involve derived genres in
the concept combination, for instance we can combine derived genres Action ⊓ Sentimental and
the above Kids ⊓ Drama.
Apart from the process of automatic knowledge generation, DENOTER is also able to reclassify
the multimedia items/episodes of RaiPlay within the novel derived genres (generated as described
in the previous section). As mentioned, indeed, each multimedia item/episode is equipped by
some information available in RaiPlay, namely: title, name of the program/episode, description
of the program/serie, description of the episode. DENOTER extracts such information and then
computes the frequencies of concepts in order to compare them with the properties of a derived
genre. If the item contains all the rigid properties and at least the 30~ of the typical properties of
the genre under consideration, then the multimedia content is classified as belonging to it.
The system has been tested in threefold evaluations showing promising results for both the
percentage of the automatically reclassified content in the novel generated classes for the user
45
Figure 1: An overview of the pipeline of DENOTER applied in the context of the RaiPlay platform.
acceptability of the recommended items. The evaluations is described in detail in [18].
5. Conclusion
In this paper, we have showed a double application, in the fields of creative goal-oriented reasoning
and content recommendations, of a logic framework explicitly built to model, in human-like
46
fashion, the concept combination phenomenon and explicitly grounded on heuristics coming from
the field of cognitive semantics. In the first application, the proposed approach has been tested
in the task of object composition and compared with the available results of the system OROC
[15] that is, to the best of our knowledge, the first system proposing a proof-of-concept procedure
for the evaluation of such tasks. In particular, we have shown how our framework is able to
generate the same results provide by the OROC system by adopting different representational and
reasoning assumptions. In addition, we have also compared the obtained results with a preliminary
evaluation involving human subjects in the task of object composition. As a further element,
we have also shown that the proposed framework is compliant with all the major mechanisms
available in the SOAR cognitive architecture and, as such, it can be effectively used to extend its
subgoaling procedures (and therefore the reasoning capabilities of the agents equipped with such
architecture).
In the second application we have used the TCL framework in DENOTER. Such system can be
used to address the very well known filter bubble effect [20], by introducing seeds of serendipity in
content discovery by users. From a the technical point of view, the system differs from the current
mainstream approaches in recommender systems that are mostly based on the comparison and
matching of visual and aural features of the content [21, 22] by adding a logic framework capable
of mapping and representing genuinely new intuitive principles influencing user preferences and
usage attitudes which cannot be derived from the pure analysis of content and/or the comparison
of similar users. Furthermore, it has a native adaptability to industrial contexts in which the
editorial input has to be merged with automatic recommendation, since both kinds of input can
be effectively processed by the same framework. In other words: it can be considered yet another
(recent) example of how the inclusion of a cognitive design perspective in the realization of
artificial systems can advance the development of current and future AI technologies.
Acknowledgments
This work has been partially supported by the project “METALLIC #2: METodi di prova per il
ragionamento Automatico per Logiche non-cLassIChe #2”, Progetto di Ricerca INdAM GNCS
2019.
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