=Paper=
{{Paper
|id=Vol-1510/paper1
|storemode=property
|title=Cognitive Programming
|pdfUrl=https://ceur-ws.org/Vol-1510/paper1.pdf
|volume=Vol-1510
|dblpUrl=https://dblp.org/rec/conf/aic/MichaelK0T15
}}
==Cognitive Programming==
https://ceur-ws.org/Vol-1510/paper1.pdf
Cognitive Programming
Loizos Michael1 , Antonis Kakas2 , Rob Miller3 , and Gyorgy Turán4
1
Open University of Cyprus, loizos@ouc.ac.cy
2
University of Cyprus, antonis@ucy.ac.cy
3
University College London, r.s.miller@ucl.ac.uk
4
University of Illinois at Chicago and MTA-SZTE
Research Group on Artificial Intelligence, gyt@uic.edu
Abstract. The widespread access to computing-enabled devices and the
World Wide Web has, in a sense, liberated the ordinary user from reliance
on technically-savvy experts. To complete this emancipation, a new way
of interacting with, and controlling the behavior of, computing-enabled
devices is needed. This position paper argues for the adoption of cognitive
programming as the paradigm for this user-machine interaction, whereby
the machine is no longer viewed as a tool at the disposal of the user, but
as an assistant capable of being supervised and guided by the user in a
natural and continual manner, and able to acquire and employ common
sense to help the user in the completion of everyday tasks. We argue
that despite the many challenges that the proposed paradigm presents,
recent advances in several key areas of Artificial Intelligence, along with
lessons learned from work in Psychology, give reasons for optimism.
1 An Emerging Need and The Overall Challenge
Today’s huge market pressure for the use of smart systems by everyone and in
every aspect of their daily life is forcing Artificial Intelligence (AI) to stand up
and deliver. What was perhaps thought out of reach in the past needs to become
a reality to satisfy the ever increasing desire of humans to use their new machines
— computer devices linked with the Internet — in their everyday activities.
Unlike anything we have seen to date, this new vision of user-machine in-
teraction will allow ordinary users without technical background to instruct or
program their devices in a natural and personalized manner, and will allow the
devices to assist (and enhance the abilities of) their users in dealing with every-
day tasks. This symbiotic relation splits the burden of communication among the
user and the device, offering a “programming paradigm for the masses”, avoid-
ing the extremes of using natural languages that are too complex for ordinary
devices, or programming languages that are too complex for ordinary users.
Early examples of such interactions already exist, ranging from the personal
assistant softwares provided by major smart-device manufacturers, to the (ex-
pected) applications for expert analysis of problems in specialized domains built
on top of the Watson engine. But perhaps the clearest example of this emerg-
ing form of interaction, which we shall call cognitive programming, is that
of searching for information on the World Wide Web. The use of web search
engines constitutes a form of programming exercised by billions, independently
of technical ability, through a programming language of keywords in natural lan-
guage, in a manner compatible with the cognitive abilities of humans. Through
their searches, users gradually develop a sense of how to improve the way they
program or instruct the search engine with queries that achieve the users’ in-
tended aim. On the other side, search engines capture the preferences or typical
behaviors of users, to help propose search queries or choose how to rank results.
We will refer to systems interacting with users through cognitive program-
ming as cognitive systems, as these systems are, in spirit at least, of the same
kind as the cognitive systems proposed relatively recently in several works in AI;
see, for example, the new journal of Advances in Cognitive Systems, the journal
of Cognitive Systems Research, and works such as [26–28, 50].
Unlike work in existing autonomous agents / systems, we think of a cognitive
system as having an operational behavior similar or parallel with that of a hu-
man personal assistant. Its domain of application is limited to certain common
everyday tasks, and its operation revolves around its interaction with its user in a
manner that is compatible with the cognitive reasoning capabilities of the latter.
To understand (and correct when needed) the reasoning process of the system,
the user expects the system to use common sense to fill-in important relevant
information that the user leaves unspecified, and to be able to keep learning
about the domain and the user’s personal preferences through their interaction.
The goal for building systems that are cognitively compatible with humans
ultimately imposes a set of considerations on cognitive programming, as this
determines the communication channel between the user and the system. The
overall challenge of developing the proposed paradigm of cognitive programming
ultimately rests on fleshing out and addressing these considerations:
– Cognitive programming should be a process akin to human-human commu-
nication. The need for detailed operational instructions should be minimized.
– There should be a level of interaction between the user and the system where
the two understand and can anticipate the behavior of each other.
– Cognitive compatibility with the user should be accommodated by acknowl-
edging the central role that natural language has in human communication,
and in the way humans store, retrieve, and use commonsense knowledge.
– Cognitive programs should develop incrementally to meet the aims of the
user through an open-ended process. Cognitive systems should be able to
learn, and be able to improve from their past interaction with the user.
– Cognitive programs should be robust, never failing, but continuously im-
proving / completing their ability to offer personalized solutions to the user,
while adapting to a possibly new or changing user position, stance, or profile.
The emphasis of this position paper is on describing the desirable character-
istics and the technical challenges resulting from the aforementioned considera-
tions. It examines the salient and foundational issues that need to be considered,
and offers possible suggestions for a first version of a cognitive programming lan-
guage. This proposal is grounded in our recent experience of trying to automate
the cognitive task of story comprehension,5 and on the comparison of the re-
sulting psychologically-informed approach with earlier work in AI for addressing
other types of scientifically-oriented problems, such as problems of diagnosis and
planning that span beyond the ordinary capabilities of human intelligence.
1.1 Scientific Position for Cognitive Programming
The scientific position underlying our approach and proposal for cognitive pro-
gramming is that symbolic AI can offer the tools needed for the aforementioned
considerations, as long as one abandons the traditional view of the role of logic
for reasoning, and one is strongly guided by work in Cognitive Psychology. To a
certain extent, then, this position takes us back to the early days of AI.
We embrace McDermott’s view in his paper “A critique of pure reason” [31],
that developing a logical theory alone — even a non-monotonic one — without
consideration of the reasoning process can not lead to human commonsense intel-
ligence. A vast amount of empirical work from Psychology (see, e.g., [12]) shows
that commonsense inferencing has a looser form than that of scientific reasoning,
and that the conventional structure and form of logical reasoning, as epitomized
by mathematical or classical logic, is not appropriate. Given strong evidence
from recent work in Psychology (see, e.g., [33]) in support of an argumentation-
based theory for human reasoning, we adopt a form of argumentation as the
basis for a cognitive system’s reasoning process. Drawing from work in Cogni-
tive Psychology (see, e.g., [13, 21, 23, 44]) on how human knowledge is (or might
be) structured and used, we base our approach on the cognitive process of com-
prehension, within which logical inference is only one component.
Although work in logic-based AI may accept, to a certain extent, the need
to deviate from strict logical reasoning (e.g., non-monotonicity, belief revision,
logic programming), efforts to automate reasoning still typically proceed on the
basis of developing proof procedures that are sound and complete against some
underlying semantics of “ideal inferences”. Unlike such work, on which cognitive
programming may be based and from which it may be guided, cognitive program-
ming shifts the emphasis from deep and elaborated reasoning to richly structured
knowledge, assuming that commonsense intelligence resides in the “complexity
of knowledge representation” rather than the “complexity of thought”. As in
many cases of Computer Science, data structures and data organizations matter
and can make all the difference in having an effective and viable solution.
2 Computational Model and System Architecture
The central notion underlying the computation of a cognitive system is that of
comprehension, a notion adopted from story or narrative text comprehension
in Cognitive Psychology (see, e.g., [22]). In our setting, comprehension proceeds
5
The system STAR: Story Comprehension through Argumentation, along with bench-
mark stories and other material, is available at: http://cognition.ouc.ac.cy/narrative/
User Query
Personal Commonsense
Profile Input Language Knowledge
Information Pre‐processing Libraries
Comprehension
Narrative Knowledge
Model
Preparation Retrieval
Construction
Response
Generation
Response
Fig. 1. General Architecture of a Cognitive System
by first combining the explicit input given by the user with information that is
available in the user’s profile (i.e., personal facts), forming an input narrative
of the task at hand. This narrative is then synthesized with information that the
system has about the domain (i.e., commonsense knowledge) and its user (i.e.,
personal preferences), leading to the construction of a comprehension model.
A comprehension model is an elaboration of the input narrative with new
information, or inferences, capturing the (or a possible) implicit meaning or
intention of the narrative. Critically, the comprehension model is coherent, and
includes only inferences that are important for successful understanding, while
omitting cluttering details and speculations. If, for example, a user enquires for
“private celebration of wedding anniversary”, it is essential for the comprehension
model to include the inference “place for two people”, but not the side inference
“married for at least one year” or the mere possibility “dinner at fancy restaurant”.
The central hypothesis of our proposed cognitive programming framework is,
then, that the availability of a comprehension model allows the system to better
act and assist its user in the requested task. The general high-level architecture
of cognitive systems that follows from this hypothesis is depicted in Figure 1.
We shall analyze the various components of this architecture in subsequent
sections. For now, we shall discuss the interaction of the user with the system.
2.1 Cognitive Programming Interaction Modes
The most basic form of user-machine interaction is querying, whereby the user,
or the cognitive assistant of some other user, or even some sensor device, inputs
a specific request or query to the cognitive system. The system then identifies or
compiles relevant commonsense knowledge, perhaps even invoking a process of
online learning, and responds with some action (e.g., a suggestion of whether to
accept or not an offer) that would help in addressing the task that has prompted
the query. When an output is thus produced by the cognitive system, another
form of interaction, that of supervising, allows the user to give feedback to the
system on the appropriateness of its output. For example, a user may override
the suggestion or decision of a cognitive assistant with or without an explanation.
The overridden output is then treated as training data for the system to learn
(better) the user’s personal opinion or preference on the particular case at hand.
Independently of any given query, the user may interact by personalizing
the cognitive system through general statements about the user’s preferences,
such as “I like to spend the evenings with my family” or “Family is more important
than work for me”. The system responds by transforming such statements in an
appropriate internal language, and recording them in the user’s profile, which,
in turn, personalizes other aspects of the user’s interaction with the system.
In the context of a particular domain of application or discourse, interaction
through guiding allows the user to offer general information that would aid the
cognitive system to understand the salient aspects of the domain. Such informa-
tion is also provided indirectly when, for instance, the user interacts with the
system in any of the preceding ways. No matter how information is provided,
guiding initiates a process to recognize concepts that are relevant and important
for the user. In turn, this information can be used to prepare relevant knowledge
on these concepts, by directing a background process of offline or batch learning
of general commonsense knowledge that is related to the particular domain.
In what is arguably the lowest (i.e., closest to the machine, and analogous to
the use of traditional programming languages) level of interaction, instructing
allows the user to input particular pieces of knowledge to the cognitive system
on how to operate or react under very specific circumstances. Such inputs are
expressed in the system’s internal language, and can be imputed directly in the
user’s personal profile or personalized knowledge libraries. We do not envisage
that this would be the prevalent way of user interaction with cognitive systems.
2.2 Illustrative Example of a Cognitive System
Suppose that Bob wishes to manage his evening work appointments with the
assistance of a cognitive system. He cognitively programs the system by guid-
ing it with domain-specific information like “dinner plans, family time, work ap-
pointments, dietary constraints”, prompting the system to gather relevant com-
monsense knowledge. Bob further personalizes the system with facts, such as
“Bob is vegetarian”, and preferences, such as “I like to spend evenings at home”,
“Customers from abroad are very important”, and “I should never miss my chil-
dren’s birthday parties”. Some of this latter type of information might have also
been learned by the system by finding regularities in Bob’s past queries to the
system (e.g., if Bob often specified the keyword “vegetarian” in past queries),
or through supervision of past proposed suggestions by the system (e.g., if Bob
often declined suggestions by the system for late dinner outside his house).
When Bob’s cognitive system receives a request from Bob’s immediate boss,
John, for “Working dinner today with John”, the system combines this input with
facts in Bob’s profile or other current information the system has from sensors,
calendars, etc., to construct an expanded input narrative. This narrative is then
comprehended through the use of the system’s commonsense libraries, and the
comprehension model is used to decide on whether the request is to be accepted.
If no additional information is given to the cognitive system, the system will
reject the request, since having dinner with John would mean going to a restau-
rant that evening, which would conflict with Bob’s preference to be at home in
the evenings. Such inferences would be supported by commonsense knowledge of
the form “Normally, working dinners are at restaurants”, and “Normally, dinner is
in the evening”. In a more advanced case the system could generate alternative
suggestions, such as to have dinner with John at home that evening. The re-
quest would also be rejected if the system were to receive from the calendar the
information that “Today is the wedding anniversary of Bob”, giving an additional
reason for Bob’s inability to have dinner with John, since “Normally, a wedding
anniversary is celebrated privately”; this piece of common sense supporting the
decision could be offered as an explanation of the system’s response.
If (possibly after the initial rejection of the request) additional information is
given that “John will be accompanied by important customers from abroad”, this
new piece of the story will be incorporated in the input narrative, leading to a
revision of the comprehension model, and to the retraction of the system’s ear-
lier decision, as now the request is supported by Bob’s preferences. The system
would then suggest to accept the request, and perhaps reschedule the celebration
of the wedding anniversary for another evening. Had further additional informa-
tion been available that “Bob’s son is having a birthday party tonight”, a further
revision would have been caused that would again reject the request, but possi-
bly suggesting an alternative plan through the use of commonsense knowledge
such as “Normally, a pre-dinner drink (and an apology) is an alternative to dinner”.
3 Foundations of Cognitive Programming
What is an appropriate theoretical model of computation and semantics of pro-
gramming that would underlie the development of the cognitive programming
paradigm? What is the form of the internal language of the cognitive system,
which would support the computational cognitive metaphor of story or narrative
text comprehension as the central form of program execution? This internal lan-
guage ultimately determines the form of representation of knowledge used by the
cognitive system. Adopting a symbolic representation raises several questions:
What is an appropriate logic and form of reasoning? Is logic alone sufficient
to capture the cognitive requirements, such as that of a natural language user-
interface and a computational model of comprehension? If not, what are the
cognitive elements that would need to accompany a logical approach?
We turn again to Cognitive Psychology (see, e.g., [16, 21, 53]) for guidance:
– Knowledge is composed of loose associations between concepts, that, unlike
logic rules, are stronger or weaker depending on the context.
– Reasoning gives rise to a single comprehension model, avoiding the cogni-
tively expensive task of considering possible non-deterministic choices.
– Reasoning proceeds lazily by drawing only inferences that are grounded di-
rectly on the explicit concepts given in the narrative, in an incremental man-
ner as parts of the narrative become available. When conflicting information
is encountered, the comprehension model is suitably revised [43].
– Cognitive economy — necessitated by human cognitive limitations, which
are bound to appear also in cognitive systems with massive knowledge li-
braries — is achieved by requiring the comprehension model to be coherent,
including inferences that are tightly interconnected, and excluding inferences
(even undisputed ones) that are peripheral to the understanding of the given
narrative [1, 15, 32, 49], or to the completion of another cognitive task [45].
The above guidelines leave, nonetheless, several key issues on the treatment
of knowledge unanswered. Below we elaborate on two of those: a more detailed
view of knowledge representation, and the process of knowledge acquisition.
3.1 Representation of Cognitive Programs
In constructing the comprehension model, the cognitive system needs to retrieve
relevant commonsense knowledge and possibly to adapt this to the narrative
(and hence to the particular query and task) at hand for subsequent reasoning.
This imposes two desired properties for knowledge representation that seem at
odds with each other: knowledge should be represented in a fashion sufficiently
flexible to be easily accessible and adaptable (e.g., in terms of the vocabulary
and syntax being used), but at the same time knowledge should be represented
in a fashion sufficiently concrete to be amenable to symbolic reasoning. We refer
to this problem of representation as the challenge of knowledge plasticity.
A way to address this challenge might be the adoption of multiple repre-
sentations for the internal language of the cognitive system, and hence, of the
commonsense knowledge that the system handles. Representations can exist, for
instance, to capture a general categorization of the knowledge, typical or exem-
plar entities and situations, detailed knowledge for specific cases, etc. Perhaps the
system’s commonsense knowledge is represented at a more general and abstract
level when it is initially acquired through offline or batch learning. When queries
are provided by the user, a form of knowledge compilation might turn the
relevant general knowledge into a task-specific form that can be directly used to
link the knowledge with the input query (and resulting narrative) for reasoning.
How the knowledge is structured in such levels and how a user input is
compiled down these levels to the specific one on which the execution / reasoning
occurs presents one of the central challenges for cognitive programming. We posit
that an argumentation perspective might be useful in capturing the important
aspects of the most specific of these levels, where knowledge is already compiled
into a form appropriate for formal reasoning. This representation framework
falls under the general scheme of abstract argumentation frameworks [11] that
have been used to formalize and study several problems in AI (see, e.g., [3, 4]),
including story comprehension [5, 9], and natural language interpretation [6].
Abstract argumentation will need to be suitably relaxed and adapted to reflect
the cognitive requirements that we have set for cognitive systems (see, e.g., [39]).
Based on our work on story comprehension [9] and our attempts to develop a
cognitive programming language for that task [10], we offer below some pointers
on what a cognitively-guided argumentation framework might look like.
Arguments are built via simple association rules, each comprising a small set
of concepts as its premise and a single concept as the conclusion that is supported
or promoted (but not necessarily logically entailed) when the premise holds. In
relation to the example discussed in Section 2.2, a relevant association rule would
be “{dinner at(Person,Place), with boss(Person)} restaurant(Place)”, capturing
the argument that having dinner with one’s boss normally happens at a restau-
rant. We view such association rules not as components of scientific theories
(e.g., of causality, of norms and obligations, of the mind), relying on elabora-
tive and careful reasoning, but rather as phenomenological manifestations of the
inferences that would follow from such theories, via a “flat” representation.
Even so, not all association rules can be applied in parallel. Different associa-
tion rules may promote conflicting conclusions, not all of which can be included
in a comprehension model. Resolving conflicts is the essence of the argumenta-
tive stance we employ. We adopt the view that association rules are annotated to
denote their (possibly relative) level of strength, so that when in conflict, these
strengths ensure that the stronger rules will draw inferences, effectively qualify-
ing (by offering a strong counter-argument to) the use of the weaker rules.
With the addition of a time dimension, such association rules are sufficiently
expressive to represent causality. Thus, if we mark the conclusion of an associa-
tion rule as holding temporally after the premise, the conclusion could correspond
to the effect that is brought about when the premise holds. Such causal links are
known from Psychology to be important in ascertaining the coherence of a com-
prehension model. Analogously, if we mark the conclusion of an association rule
as holding temporally before the premise, the conclusion could correspond to an
explanation of why the premise came to be. Drawing such explanatory inferences
(when justified to do so) is again critical in the process of comprehension.
Such aspects of causality in world knowledge have featured prominently in the
foundations of Artificial Intelligence (cf. the Situation Calculus [30], the Event
Calculus [25], and several action languages [14, 19, 29, 46]). The central problems
of frame, ramification, and qualification will need to be addressed within the cog-
nitive programming framework, but only in a simplified and qualitative form, as
it suffices for our treatment of cognitive programs as phenomenological theories.
3.2 Acquisition of Cognitive Programs
Key in a cognitive system’s working is the availability of relevant knowledge, or
cognitive programs. Even though the user could contribute to this knowledge by
directly instructing the system, we envision that the main mechanism through
which cognitive programs would be acquired will be offline or batch learning.
The most promising source of training material for learning commonsense
knowledge is currently natural language text, both because of the existence of
parsing and processing tools that are more advanced than those that exist for
other media (e.g., images), but also because of the high prevalence of textual
corpora. The World Wide Web has, typically, played the role of such a textual
corpus for machine learning work seeking to extract facts (see, e.g., [41]). When
seeking to extract, instead, knowledge appropriate for reasoning, an additional
consideration comes into play: knowledge encoded in text from the World Wide
Web is biased and incomplete in several ways with respect to our commonsense
real-world knowledge, and would be more aptly called websense [36]. We posit,
however, that certain deficiencies that a cognitive system could have by employ-
ing websense would be overcome through the user’s feedback and supervision.
Acquisition of knowledge could proceed in several ways. For one, the cogni-
tive system may memorize fragments of text that describe exemplars of certain
concepts or scenarios (e.g., a typical restaurant scenario). In a somewhat more
structured form, the cognitive system may compute and store statistics about
word co-occurrences, e.g., in the form of n-grams, or in the form of frequencies
of words appearing in a piece of text conditioned on certain other words also ap-
pearing. This last form of statistical information can be interpreted as a weighted
association rule, with the weight indicating the “strength” or “probability” of
the association holding. In an even more structured form, statistics as above can
be stored not on words, but on relations extracted by parsing the text.
Beyond statistical information, one can attempt to learn reasoning rules over
words or relations, using typical machine learning techniques. Some such tech-
niques represent learned rules in a form understandable by humans (e.g., DNF
formulas). Recent work has shown, in fact, that one can learn not only deductive
rules, but also abductive ones, which provide possible explanations given a cer-
tain input to be explained [18]. Learning causal rules can also proceed naturally
by treating consecutive sentences in a textual corpus as the before and after
states needed for causal learnability [35]. Treating fragments of texts as partial
observations of some underlying, even if unknown, truth or reality can be shown
to guarantee [34] that rules learned in this manner will draw inferences that are
not explicitly stated in, but follow from, a given piece of text. This task, known
as textual entailment [8], contributes to one of the necessary processes (namely,
the drawing of relevant inferences) for constructing a comprehension model.
The amount of knowledge that can be extracted from text is massive, and
measures need to be taken to account for this. Section 2.1 has already pointed
out that the user guides, explicitly or implicitly, the cognitive system on what
concepts the system needs to focus on, and in turn these concepts determine what
training material the system will seek for learning knowledge. Even with such
guidance, the system may need to refrain from learning knowledge in the most
specific form possible, since that would commit the knowledge to a very rigid
representation that could not be used later in the context of different queries.
Instead, the system should probably choose to retain the learned knowledge in
a general representation, some examples of which we have discussed above.
This type of batch and query-independent learning could operate continu-
ously, with the learned knowledge guiding its further development by identifying
those concepts for which more training is needed. This process ensures, then, the
gradual improvement of a system’s cognitive programs, and hence their perfor-
mance. When a query is posed, the process of knowledge compilation may invoke
a further (online) form of learning, treating the offline-learned general knowledge
as training data. This query-driven learning is much more focused (and could, in
fact, be done implicitly [17]), and should, therefore, be sufficiently efficient to be
carried out in real time between the user posing a query and receiving a response.
The results of this online learning may be stored, and be reused for future query
answering. Supervision by the user may provide additional training material for
online learning, which would produce, therefore, user-specific knowledge.
In all cases, learning should proceed in a manner that anticipates reasoning.
Valiant’s Probably Approximately Correct (PAC) semantics for learning and rea-
soning [47, 48] points to how one could establish formal guarantees on the quality
of learned cognitive programs and the comprehension models and inferences they
produce. Recent work has proposed PAC semantics for two situations that are of
particular interest to cognitive systems: when reasoning involves the chaining of
multiple pieces of knowledge [37]; and, when a user’s interaction with a cognitive
system is personalized by learning to predict the user’s intentions [38, 40].
4 Major Challenges for Cognitive Programming
Developing cognitive systems through the cognitive programming paradigm poses
major technical challenges. We group and summarize below certain such chal-
lenges that would need to be overcome to make progress in this direction.
User-Machine Interaction. Cognitive systems need to interact with human
users in a natural way through some fragment of natural language. Hence, the
natural language processing capabilities of the supporting modules of cognitive
programming are important. In particular, central questions include:
– How do we structure and restrict the complexity of natural language for the
user-interface fragment of natural language, without, on the one hand, losing
the expressiveness required by the applications, and while keeping, on the
other hand, a form of natural communication with human users?
– How can we use existing natural language processing (NLP) systems for
the syntactic and grammatical analysis of the user input to ascertain the
concepts involved and to extract the narrative information? The use of better
NLP tools should help us develop incrementally improved cognitive systems.
– How does the user become aware of the language and knowledge capabilities
of the underlying cognitive programming framework? How can we develop
useful schemes of dialogues between the user and cognitive systems for user
feedback and for natural forms of supervision of the system by the user?
Reasoning with Common Sense. The basic form of argumentative cognitive
reasoning and comprehension depends critically on many factors, when this is
to be scaled up to be applied in many (if not all the) domains of discourse of
common sense. The major questions that need concrete technical answers are:
– Does commonsense knowledge have a generic and task-independent vocabu-
lary and form? What is an appropriate such form and how is this adapted (in
real time, through knowledge compilation) into a useful task-specific form?
In particular, how do we address the need for syntactic plasticity of com-
monsense knowledge, so that it can be adapted in a manner syntactically
compatible with the vocabulary that the current input narrative is using?
– How are relevant parts of commonsense knowledge identified efficiently and
reliably given an input narrative? In particular, how do we address the need
for conceptual plasticity of commonsense knowledge, so that the concepts
referred to in the input narrative are matched to concepts in the knowledge
base? Is a meta-level form of “context indexing” of the knowledge needed?
– How do we integrate effectively the “pure reasoning” with the process of
comprehension, while being guided by the central principle of coherence?
Acquiring Common Sense. Given that we have an appropriate representa-
tion for commonsense knowledge, we are then faced with the challenge of how
to automatically learn and populate a commonsense library. Questions include:
– Is an offline or batch learning process for commonsense knowledge acquisition
the only form of learning required, or do we also need a form of online learning
at the time of query processing and knowledge compilation?
– How do we distinguish learned user-specific knowledge from learned generic
commonsense knowledge given that the user supervises both processes, and
how could learned knowledge be reused across users and cognitive systems?
– What are the main technical problems of “mining” commonsense association
rules from the World Wide Web? What NLP techniques, search and down-
load tools, storage and indexing schemes would be required? How do we
overcome the possibly biased and incomplete nature of learned knowledge?
– How do we learn the annotations and priority tags of commonsense associa-
tion rules? Can this process be automated, or is it ultimately user-specific?
To address many of these challenges, further empirical study with the help of
Cognitive Psychology will be needed to help reveal possible answers and guide
the development of the computational framework. The availability of a compu-
tational framework would then facilitate the experimental examination of the
computational viability and effectiveness of various guidelines in improving the
cognitive programming framework and the programming experience of the users.
In particular, the central and major issues of knowledge plasticity and knowledge
compilation are amenable to empirical psychological investigation.
In general, the development of cognitive programming needs to be informed
and guided by the psychological understanding at different levels of human cog-
nitive processes. Understanding how the mind operates at some higher concep-
tual level when dealing with everyday cognitive tasks can help us in developing
possible models of computation in cognitive programming. On the other hand,
understanding how humans introspectively perceive or understand the opera-
tion of their cognitive processes can help us develop human-compatible models
of computation: models of computation that humans can naturally relate to.
5 Concluding Remarks
Ideas and proposals related to one form or another of cognitive systems go back
to the very beginning of the history of AI, and it would be an interesting topic
in itself to explore the development and confluence of these ideas. Among work
carried out in more recent years on cognitive computing and systems, Watson is,
perhaps, closest to a complete system, and has attracted the most attention from
the media. Unlike its emphasis towards “help[ing] human experts make better
decisions by penetrating the complexity of Big Data”,6 our proposal focuses on
assisting ordinary people by supporting their everyday decision making.
Although both Watson and our envisioned systems seek to solve a cognitive
task, the difference in emphasis outlined above suggests that for the latter sys-
tems it is crucial that the problem-solving process itself be cognitive, inspired
by human heuristics and transparent to the ordinary people’s way of thinking.
It could be argued that the label “cognitive” should be reserved for such types
of systems, and not be conferred to every system that solves a cognitive task.
Adopting this more stringent view of cognitive systems points to a second —
in addition to developing intelligent machines — end for building them. Through
their operation, cognitive systems could be used to empirically validate or fal-
sify the theoretical models they implement, supporting the scientific process of
hypothesizing, predicting, and revising. This iterative process would allow AI to
contribute to the refinement of psychological theories of human cognition.
Following a vision where humans and machines share a similar level of com-
mon sense, we have proposed cognitive programming as a means to build cogni-
tive systems. Cognitive programming adopts the view of a machine as a personal
assistant: a human asks for the completion of a task, perhaps without fully and
unambiguously specifying what is needed, but relying on the assistant’s experi-
ence, and, ultimately, common sense, to perform the task. Cognitive program-
ming aims to bring the flexibility of traditional programming to the masses of
existing technology users, enabling them to view their personal devices as novice
assistants, amenable to training and personalization through natural interaction.
Our proposal offers a blueprint of what needs to be done and the challenges
that one will have to face. We are optimistic that it can be realized to a large
extent by building on existing techniques and knowhow from Artificial Intelli-
gence, especially when one takes a pragmatic view by synthesizing the theory
and methods of AI with empirical results and ideas from Cognitive Psychology.
Unsurprisingly, the representation and reasoning requirements for cognitive
programming are reminiscent of those of production rules as one finds in Compu-
tational Cognitive Psychology (see, e.g., [2, 20, 52]). For cognitive programming,
6
See, for instance, this website: http://www.research.ibm.com/cognitive-computing/
production rules need to include the element of causality in their representation,
be enhanced with a declarative form of representing and handling conflicts, and
use some notion of (relative) strength of knowledge — or, of the arguments built
from the underlying commonsense knowledge — when drawing inferences.
Logic Programming, and recent developments from this [24], have moved
in this direction of production or reactive systems with such enhancements, but
remain largely bound to the strict formal logical semantics. Similarly, frameworks
for autonomous agents, such as BDI agents [42] and robotic agent programming
[7], which aim amongst other things to give cognitive abilities to agents, also rely
on strict logical or operational semantics. These approaches serve, therefore, a
different class of problems from those aimed to by cognitive systems based on
commonsense knowledge, and for which the role of comprehension is important.
One may argue that progress on natural language understanding would suffice
to realize our vision of cognitive programming. Despite the important role of such
progress, a fully automated natural language system would seem to require a
machine architecture similar to that of the human brain. Given the gap between
the formal logic-driven machine architectures of today (with long, rigid, and
error-intolerant chains of computation — a limitation already identified by von
Neumann [51]), and the cognitive capabilities and constraints of the human mind,
our proposal of cognitive programming hopes to provide the middle-ware needed
today to move closer to the ideal of an automated natural language system.
Acknowledgements
We are grateful to our colleague Irene-Anna Diakidoy (Department of Psychol-
ogy, University of Cyprus) for her collaboration in our joint work on story com-
prehension, and the invaluable help that she has given us in our effort to under-
stand the computational implications of relevant work in Cognitive Psychology.
We also wish to thank Hugo Mercier and Francesca Toni for many valuable
discussions on the psychology of reasoning and the nature of cognitive systems.
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