Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
Functional and Structural Models of Commonsense Reasoning in Cognitive Archit...Antonio Lieto
The document provides an overview of functional and structural models of commonsense reasoning in cognitive architectures. It discusses several approaches to commonsense reasoning including semantic networks, frames, scripts, and default logic. It also discusses different levels of representation including conceptual spaces, typicality, and compositionality. The document proposes dual process models that integrate heterogeneous representations like prototypes and exemplars. It presents computational models like Dual PECCS and TCL that implement aspects of commonsense reasoning through integrated and connected representations.
Cognitive Paradigm in AI - Invited Lecture - Kyiv/Kyev - LietoAntonio Lieto
1) The document discusses the cognitive paradigm in artificial intelligence research and cognitively inspired AI systems.
2) Cognitively inspired AI systems are designed based on insights from human and animal cognition, using structural constraints from cognitive science.
3) Examples of cognitively inspired AI systems discussed include GPS, semantic networks, the RM model of past-tense acquisition, and cognitive architectures like Soar and ACT-R.
The document discusses a commonsense reasoning framework called TCL that integrates typicality, probabilities, and cognitive heuristics. TCL extends description logics with a typicality operator and probabilistic semantics to model prototypical properties. It also uses cognitive heuristics like head-modifier to identify plausible mechanisms for concept combination. The framework has been applied to generate novel content and classify emotions, with encouraging results explaining item-emotion associations for the deaf community.
Knowledge Capturing via Conceptual Reframing: A Goal-oriented Framework for K...Antonio Lieto
The document presents a goal-oriented framework called GOCCIOLA that can generate novel knowledge by recombining concepts in a dynamic way to solve problems. GOCCIOLA uses a logic called TCL that can reason about typical properties of concepts and their combinations. It evaluates plausible scenarios for combining concepts using probabilities and heuristics from cognitive semantics. GOCCIOLA was tested on a concept composition task and able to provide solutions to goals by suggesting new concept combinations. The system has applications in computational creativity and cognitive architectures.
Lieto - Book Presentation Cognitive Design for Artificial Minds (AGI Northwes...Antonio Lieto
The document discusses a book titled "Cognitive Design for Artificial Minds" by Antonio Lieto. It includes quotes from several professors praising the book for proposing a re-unification of artificial intelligence and cognitive science. The book explores connections between AI modeling techniques and cognitive science methods. It also provides an overview of cognitive architectures and argues that a biologically/cognitively inspired approach can help develop next generation AI systems beyond deep learning. The document discusses challenges in developing a standard model of cognition and the need for collaboration across the AI and cognitive science communities.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Commonsense reasoning as a key feature for dynamic knowledge invention and co...Antonio Lieto
This document discusses commonsense reasoning and its importance for computational creativity and knowledge invention. It provides an overview of past AI and cognitive science approaches to commonsense reasoning such as semantic networks, frames, and default logic. It then presents the TCL (Typicality Description Logic) framework, which extends description logics with typicality, probabilities, and cognitive heuristics to model commonsense conceptual combination. The framework is applied to generate novel concepts to achieve goals and to dynamically classify multimedia content. Evaluations show it effectively reclassifies content and generates recommendations that users and experts find high quality.
Extending the knowledge level of cognitive architectures with Conceptual Spac...Antonio Lieto
Extending the knowledge level of cognitive architectures with Conceptual Spaces (+ a case study with Dual-PECCS: a hybrid knowledge representation system for common sense reasoning). Talk given at Stockholm, September 2016.
Functional and Structural Models of Commonsense Reasoning in Cognitive Archit...Antonio Lieto
The document provides an overview of functional and structural models of commonsense reasoning in cognitive architectures. It discusses several approaches to commonsense reasoning including semantic networks, frames, scripts, and default logic. It also discusses different levels of representation including conceptual spaces, typicality, and compositionality. The document proposes dual process models that integrate heterogeneous representations like prototypes and exemplars. It presents computational models like Dual PECCS and TCL that implement aspects of commonsense reasoning through integrated and connected representations.
Cognitive Paradigm in AI - Invited Lecture - Kyiv/Kyev - LietoAntonio Lieto
1) The document discusses the cognitive paradigm in artificial intelligence research and cognitively inspired AI systems.
2) Cognitively inspired AI systems are designed based on insights from human and animal cognition, using structural constraints from cognitive science.
3) Examples of cognitively inspired AI systems discussed include GPS, semantic networks, the RM model of past-tense acquisition, and cognitive architectures like Soar and ACT-R.
The document discusses a commonsense reasoning framework called TCL that integrates typicality, probabilities, and cognitive heuristics. TCL extends description logics with a typicality operator and probabilistic semantics to model prototypical properties. It also uses cognitive heuristics like head-modifier to identify plausible mechanisms for concept combination. The framework has been applied to generate novel content and classify emotions, with encouraging results explaining item-emotion associations for the deaf community.
Knowledge Capturing via Conceptual Reframing: A Goal-oriented Framework for K...Antonio Lieto
The document presents a goal-oriented framework called GOCCIOLA that can generate novel knowledge by recombining concepts in a dynamic way to solve problems. GOCCIOLA uses a logic called TCL that can reason about typical properties of concepts and their combinations. It evaluates plausible scenarios for combining concepts using probabilities and heuristics from cognitive semantics. GOCCIOLA was tested on a concept composition task and able to provide solutions to goals by suggesting new concept combinations. The system has applications in computational creativity and cognitive architectures.
Lieto - Book Presentation Cognitive Design for Artificial Minds (AGI Northwes...Antonio Lieto
The document discusses a book titled "Cognitive Design for Artificial Minds" by Antonio Lieto. It includes quotes from several professors praising the book for proposing a re-unification of artificial intelligence and cognitive science. The book explores connections between AI modeling techniques and cognitive science methods. It also provides an overview of cognitive architectures and argues that a biologically/cognitively inspired approach can help develop next generation AI systems beyond deep learning. The document discusses challenges in developing a standard model of cognition and the need for collaboration across the AI and cognitive science communities.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Commonsense reasoning as a key feature for dynamic knowledge invention and co...Antonio Lieto
This document discusses commonsense reasoning and its importance for computational creativity and knowledge invention. It provides an overview of past AI and cognitive science approaches to commonsense reasoning such as semantic networks, frames, and default logic. It then presents the TCL (Typicality Description Logic) framework, which extends description logics with typicality, probabilities, and cognitive heuristics to model commonsense conceptual combination. The framework is applied to generate novel concepts to achieve goals and to dynamically classify multimedia content. Evaluations show it effectively reclassifies content and generates recommendations that users and experts find high quality.
Extending the knowledge level of cognitive architectures with Conceptual Spac...Antonio Lieto
Extending the knowledge level of cognitive architectures with Conceptual Spaces (+ a case study with Dual-PECCS: a hybrid knowledge representation system for common sense reasoning). Talk given at Stockholm, September 2016.
The document summarizes a talk given by Valeria de Paiva on intuitionistic modal logic 15 years after its initial development. It discusses the early work developing systems of intuitionistic modal logic like Constructive S4 and their proof theories. It also describes the formation of the Intuitionistic Modal Logic and Applications association aimed at bringing together researchers from different fields to share tools and information. However, the goal of this association being fully realized, with communities still largely talking past each other, is assessed as not having been attained so far.
This document summarizes the history of the Intuitionistic Modal Logic and Applications conference series. It discusses the five previous conferences held between 1999-2008. It then outlines the program for IMLA2011, including a list of talks. Finally, it mentions past IMLA publications and future plans, such as a potential special journal issue and timeline for an open call for papers.
This document discusses constructive modal logics and open questions in the field. It describes two main families of constructive modal logics, CK and IK, which differ in their proof-theoretical properties. Developing satisfactory proof theories for these logics has been challenging, requiring augmentations to sequent systems. The document also notes that while IK logics have better model-theoretic properties, CK logics are better suited for lambda calculus interpretations. Overall, the document advocates for further work to develop a unified framework that can capture both families of logics along with categorical semantics.
"Objective fiction: the semantic construction of web reality" talks about current challenges for semantic technologies, and the Semantic Web in particular, focusing on cognitive and social dimensions of human semantics.
The document discusses modalities in linear logic and dialectica categories. It motivates studying (co)monads and (co)algebras as constructive modalities in linear logic. It describes how the linear logic bang modality ! can be modeled as a comonad in dialectica categories. Specifically, in the dialectica category Dial2(C), ! is modeled as a cofree comonad. This provides a model of intuitionistic linear logic with both linear and non-linear connectives. In the simpler category DDial2(C), modeling the bang requires composing two comonads.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
This document provides an overview of knowledge representation and networked schemes in artificial intelligence. It discusses several topics:
- Knowledge representation is how knowledge is encoded in a computer-understandable form in an AI system's knowledge base.
- Networked schemes like semantic nets and conceptual graphs represent knowledge using graphs with nodes for concepts and relationships.
- Semantic nets use nodes for concepts/objects and labeled arcs for relationships between nodes. Conceptual graphs also use concept and relationship nodes but have additional rules for node connections.
- Both schemes allow inheritance of features through restriction and joining operations on the graphs. They can represent logical operations and support reasoning.
This document provides an introduction to knowledge representation in artificial intelligence. It discusses how knowledge representation and reasoning forms the basis of intelligent behavior through computational means. The key types of knowledge that need to be represented are defined, including objects, events, facts, and meta-knowledge. Different types of knowledge such as declarative, procedural, structural and heuristic knowledge are explained. The importance of knowledge representation for modeling intelligent behavior in agents is highlighted. The requirements for effective knowledge representation including representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency are outlined. Propositional logic is introduced as the simplest form of logic using propositions.
The document discusses the "golden years" of AI in the 1960s, including notable projects like ELIZA, Shakey the robot, and the Blocks World simulations with SHRDLU. It also covers the inherent limitations of computability, such as the halting problem showing that some decision problems cannot be solved algorithmically. Finally, it introduces the idea of a Physical Symbol System and the hypothesis that such systems are capable of general intelligent action if they can designate and interpret physical symbols.
Artificial intelligence and knowledge representationSajan Sahu
The document discusses artificial intelligence and knowledge representation. It describes how computers can be made intelligent through speed of computation, filtering responses, using algorithms and neural networks. It also discusses knowledge representation techniques in AI like propositional logic, semantic networks, frames, predicate logic and nonmonotonic reasoning. The document provides examples and applications of AI like pattern recognition, robotics and natural language processing. It also discusses some fundamental problems of AI.
This document provides an overview and introduction to the course "Knowledge Representation & Reasoning" taught by Ms. Jawairya Bukhari. It discusses the aims of developing skills in knowledge representation and reasoning using different representation methods. It outlines prerequisites like artificial intelligence, logic, and programming. Key topics covered include symbolic and non-symbolic knowledge representation methods, types of knowledge, languages for knowledge representation like propositional logic, and what knowledge representation encompasses.
The document discusses the history and connections between logic, proofs, programs, and category theory. It notes that Hilbert's program to formalize mathematics led to the development of proof theory and Gentzen's natural deduction and sequent calculus systems. The Curry-Howard correspondence showed that proofs and programs are closely related through the use of lambda calculus. Category theory provides a unified framework where types represent logical formulas, terms represent proofs, and reductions represent proof normalization. Categorical proof theory models proofs as first-class citizens.
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...ijcsit
The process of building ontology is a very
complex and time
-
consuming process
especially when dealing
with huge amount of data. Unfortunately current
marketed
tools are very limited and don’t meet
all
user
needs.
Indeed, t
hese software build the core of the ontology from initial data that generates
a
big number of
information.
In this paper, we
aim to resolve these problems
by adding an extension to the well known
ontology editor Protégé in order to work towards a complete
FCA
-
based framework
which resolves the
limitation of other tools in
building fuzzy
-
ontology
.
W
e will give
, in this paper
, some
details on
our
sem
i
-
automat
ic collaborative tool
called FOD Tab Plug
-
in
which
takes into consideration another degree of
granularity in the process of generation
.
In fact, i
t follows a bottom
-
up strategy based on conceptual
clustering, fuzzy logic and Formal Concept Analysis (FCA) a
nd it defines ontology between classes
resulting from a preliminary classification of data and not from the initial large amount of data
.
Artificial intelligence and knowledge representationLikan Patra
Artificial intelligence uses algorithms and knowledge representation to solve problems in a manner inspired by human intelligence. Knowledge representation involves using formal symbolic logic and structures like semantic networks and frames to represent knowledge. Different representation techniques exist for propositions, predicates, rules and nonmonotonic reasoning. Challenges for AI include acquiring knowledge autonomously, representing human experiences, and fully transferring human knowledge through communication.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
The document summarizes a talk given by Valeria de Paiva on intuitionistic modal logic 15 years after its initial development. It discusses the early work developing systems of intuitionistic modal logic like Constructive S4 and their proof theories. It also describes the formation of the Intuitionistic Modal Logic and Applications association aimed at bringing together researchers from different fields to share tools and information. However, the goal of this association being fully realized, with communities still largely talking past each other, is assessed as not having been attained so far.
This document summarizes the history of the Intuitionistic Modal Logic and Applications conference series. It discusses the five previous conferences held between 1999-2008. It then outlines the program for IMLA2011, including a list of talks. Finally, it mentions past IMLA publications and future plans, such as a potential special journal issue and timeline for an open call for papers.
This document discusses constructive modal logics and open questions in the field. It describes two main families of constructive modal logics, CK and IK, which differ in their proof-theoretical properties. Developing satisfactory proof theories for these logics has been challenging, requiring augmentations to sequent systems. The document also notes that while IK logics have better model-theoretic properties, CK logics are better suited for lambda calculus interpretations. Overall, the document advocates for further work to develop a unified framework that can capture both families of logics along with categorical semantics.
"Objective fiction: the semantic construction of web reality" talks about current challenges for semantic technologies, and the Semantic Web in particular, focusing on cognitive and social dimensions of human semantics.
The document discusses modalities in linear logic and dialectica categories. It motivates studying (co)monads and (co)algebras as constructive modalities in linear logic. It describes how the linear logic bang modality ! can be modeled as a comonad in dialectica categories. Specifically, in the dialectica category Dial2(C), ! is modeled as a cofree comonad. This provides a model of intuitionistic linear logic with both linear and non-linear connectives. In the simpler category DDial2(C), modeling the bang requires composing two comonads.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
This document provides an overview of knowledge representation and networked schemes in artificial intelligence. It discusses several topics:
- Knowledge representation is how knowledge is encoded in a computer-understandable form in an AI system's knowledge base.
- Networked schemes like semantic nets and conceptual graphs represent knowledge using graphs with nodes for concepts and relationships.
- Semantic nets use nodes for concepts/objects and labeled arcs for relationships between nodes. Conceptual graphs also use concept and relationship nodes but have additional rules for node connections.
- Both schemes allow inheritance of features through restriction and joining operations on the graphs. They can represent logical operations and support reasoning.
This document provides an introduction to knowledge representation in artificial intelligence. It discusses how knowledge representation and reasoning forms the basis of intelligent behavior through computational means. The key types of knowledge that need to be represented are defined, including objects, events, facts, and meta-knowledge. Different types of knowledge such as declarative, procedural, structural and heuristic knowledge are explained. The importance of knowledge representation for modeling intelligent behavior in agents is highlighted. The requirements for effective knowledge representation including representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency are outlined. Propositional logic is introduced as the simplest form of logic using propositions.
The document discusses the "golden years" of AI in the 1960s, including notable projects like ELIZA, Shakey the robot, and the Blocks World simulations with SHRDLU. It also covers the inherent limitations of computability, such as the halting problem showing that some decision problems cannot be solved algorithmically. Finally, it introduces the idea of a Physical Symbol System and the hypothesis that such systems are capable of general intelligent action if they can designate and interpret physical symbols.
Artificial intelligence and knowledge representationSajan Sahu
The document discusses artificial intelligence and knowledge representation. It describes how computers can be made intelligent through speed of computation, filtering responses, using algorithms and neural networks. It also discusses knowledge representation techniques in AI like propositional logic, semantic networks, frames, predicate logic and nonmonotonic reasoning. The document provides examples and applications of AI like pattern recognition, robotics and natural language processing. It also discusses some fundamental problems of AI.
This document provides an overview and introduction to the course "Knowledge Representation & Reasoning" taught by Ms. Jawairya Bukhari. It discusses the aims of developing skills in knowledge representation and reasoning using different representation methods. It outlines prerequisites like artificial intelligence, logic, and programming. Key topics covered include symbolic and non-symbolic knowledge representation methods, types of knowledge, languages for knowledge representation like propositional logic, and what knowledge representation encompasses.
The document discusses the history and connections between logic, proofs, programs, and category theory. It notes that Hilbert's program to formalize mathematics led to the development of proof theory and Gentzen's natural deduction and sequent calculus systems. The Curry-Howard correspondence showed that proofs and programs are closely related through the use of lambda calculus. Category theory provides a unified framework where types represent logical formulas, terms represent proofs, and reductions represent proof normalization. Categorical proof theory models proofs as first-class citizens.
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...ijcsit
The process of building ontology is a very
complex and time
-
consuming process
especially when dealing
with huge amount of data. Unfortunately current
marketed
tools are very limited and don’t meet
all
user
needs.
Indeed, t
hese software build the core of the ontology from initial data that generates
a
big number of
information.
In this paper, we
aim to resolve these problems
by adding an extension to the well known
ontology editor Protégé in order to work towards a complete
FCA
-
based framework
which resolves the
limitation of other tools in
building fuzzy
-
ontology
.
W
e will give
, in this paper
, some
details on
our
sem
i
-
automat
ic collaborative tool
called FOD Tab Plug
-
in
which
takes into consideration another degree of
granularity in the process of generation
.
In fact, i
t follows a bottom
-
up strategy based on conceptual
clustering, fuzzy logic and Formal Concept Analysis (FCA) a
nd it defines ontology between classes
resulting from a preliminary classification of data and not from the initial large amount of data
.
Artificial intelligence and knowledge representationLikan Patra
Artificial intelligence uses algorithms and knowledge representation to solve problems in a manner inspired by human intelligence. Knowledge representation involves using formal symbolic logic and structures like semantic networks and frames to represent knowledge. Different representation techniques exist for propositions, predicates, rules and nonmonotonic reasoning. Challenges for AI include acquiring knowledge autonomously, representing human experiences, and fully transferring human knowledge through communication.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
Invited Tutorial - Cognitive Design for Artificial Minds AI*IA 2022Antonio Lieto
This document provides an overview of cognitive design for artificial minds. It discusses how cognitive artificial systems are inspired by human and natural cognition. The key points made are:
- Cognitive artificial systems are inspired by human and natural cognition to be more general and versatile than standard AI systems.
- Examples of cognitively inspired AI systems include ACT-R, Soar, and systems developed using the subsumption architecture.
- Cognitively inspired systems differ from standard AI in that they aim to have explanatory power for human cognition through structural models of cognitive processes and representations.
- Such systems can be used to test cognitive theories, provide human-like capabilities, and potentially lead to more general artificial intelligence.
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
This document discusses different views on pragmatic rationality and the interfaces between pragmatics, semantics, and other domains like neurology and cognitive science. It summarizes debates around Gricean rationality and different theories like Relevance Theory that propose alternative models of pragmatic reasoning and inference. Experimental evidence from psycholinguistics is also discussed regarding how it relates to and constrains theoretical models of pragmatic processing.
Towards which Intelligence? Cognition as Design Key for building Artificial I...Antonio Lieto
The document discusses approaches to building artificial intelligence systems based on human cognition. It argues that AI should focus on high-level cognitive functions like humans exhibit full intelligence. A cognitive AI approach models heuristics and bounded rationality used by humans. The document presents a case study of a common sense reasoning system that integrates heterogeneous conceptual representations like prototypes and exemplars, and uses a dual process of reasoning. The system is evaluated against human responses in categorization tasks with 84% accuracy, providing insights to refine the cognitive theory.
Multiple representations and visual mental imagery in artificial cognitive sy...University of Huddersfield
1) The document discusses multiple representations in artificial cognitive systems, including both external representations like diagrams and internal mental representations like visual mental imagery.
2) It presents examples of how problems can be solved using either a mathematical/propositional representation or a visual/imagery-based representation.
3) Leading cognitive architectures are discussed in terms of how they have begun to incorporate multiple representations, with some exploring non-symbolic, array-based representations to model processes involved in visual mental imagery.
This document discusses logics of context and modal type theories. It begins by providing some background and caveats. It then presents a motivating example about reasoning about claims within a report. The document discusses tasks involving contextual structure and reasoning across contexts. It advocates for using proof theory and natural deduction systems when designing logics of context. It presents some approaches to modeling contexts and modality, including McCarthy's original ideas. It discusses properties that are important for logics of context, such as normalization. It provides overviews of some existing logics of context and compares their properties and limitations.
Vertical integration of computational architectures - the mediator problemYehor Churilov
1. The document discusses the problem of integrating computational architectures for artificial intelligence. There is a major gap between low-level sensory representations and higher-level cognitive functions that cannot be bridged by existing two-tier architectures alone.
2. It proposes that a conceptually independent architectural layer is needed to act as a mediator between the different representation levels. This would help address issues around increasing cognitive abilities that demand greater integration across architectures.
3. A second problem is the height of the integration platform - a fully integrated platform is needed at a higher level than currently exists for modular hybrid systems. The document outlines approaches to solving the mediator problem and facilitating greater platform integration through more unified computing methods.
The Role Of Ontology In Modern Expert Systems Dallas 2008Jason Morris
The document discusses the role of ontologies in modern expert system development. It provides background on expert systems and ontologies, explaining that ontologies define domains of knowledge and are used to encapsulate domain knowledge for use in expert systems. The document outlines the process of developing ontologies, including identifying concepts and relationships in a domain. It also provides an example of an expert system called SINFERS that uses ontologies to select soil property prediction models.
A Computational Framework for Concept Representation in Cognitive Systems and...Antonio Lieto
This document proposes a framework for representing concepts in cognitive systems called "concepts as heterogeneous proxytypes". It suggests concepts have multiple representations, including classical, prototypical, exemplar-based and theory-based. These representations are stored separately but can be combined. The framework represents concepts computationally using different frameworks like symbols, conceptual spaces and neural networks. It aims to test if this heterogeneous proxytype hypothesis can explain human concept identification and retrieval by implementing it in cognitive architectures.
This document summarizes the history and development of modal logics from Frege to the present day. It discusses key figures like Hilbert, Gentzen, Prawitz, Martin-Lof, and Girard and their contributions to logic systems. It also covers developments in intuitionistic modal logics by researchers like Simpson, Nerode, and the IMLA community. Challenges are noted in developing modal type theories that satisfy properties like substitution while accounting for diverse applications and perspectives. Overall modal logic remains an active area with opportunities for further work.
Philosophy of science summary presentation engelbyDavid Engelby
Philosophy of science can be summarized in 3 domains:
1) Epistemology - the study of knowledge and justified belief, including what we know and how we know it.
2) Ontology - what exists and how we conceptualize and represent domains of knowledge.
3) Methodology - the framework for combining theories and approaches, including specific research methods.
Key concepts in philosophy of science include knowledge, truth, explanation, concepts, constructs, variables, and research methodology.
This document provides an overview of ontologies and the semantic web. It defines ontologies as formal specifications of conceptualizations that are shared between people and computers. Ontologies provide a common vocabulary and conceptual structure to facilitate understanding between humans and machines. They allow different systems and communities to work together by providing shared definitions of concepts and relationships. The development of ontologies and the semantic web aims to make web resources more computer-readable and enable machines to better understand and process online information.
This document provides an overview of constructive modal logics. It discusses motivation for considering constructive modalities and briefly outlines the history of research at the intersection of constructive and modal logic. The talk then explores several approaches to developing constructive modal, hybrid, and description logics, including translating logics into intuitionistic first-order logic or constructive modal logics like IK or CK. Related work is also mentioned. The discussion emphasizes that more work is needed to establish criteria for identifying the best constructive systems and to apply these logics to areas like temporal reasoning.
Ex nihilo nihil fit: A COMMONSENSE REASONING FRAMEWORK FOR DYNAMIC KNOWLEDGE...Antonio Lieto
The document presents a commonsense reasoning framework called TCL that can be used for dynamic knowledge invention through conceptual combination and blending. TCL integrates typicality, probabilities and cognitive heuristics in a description logic framework. It allows modeling of non-monotonic inferences like induction, abduction and default reasoning. The framework has been applied to tasks like goal-oriented knowledge generation, affective computing and its use in robotics is discussed.
Representation of ontology by Classified Interrelated object modelMihika Shah
1. The document discusses representing ontology using the Classified Interrelated Object Model (CIOM) data modeling technique. CIOM represents ontology components like classes, subclasses, attributes, and relationships between classes.
2. Key components of an ontology like classes, subclasses, attributes, and inter-class relationships are described and examples are given of how each would be represented using CIOM notation.
3. CIOM provides a general purpose methodology for representing ontologies using existing database technologies and overcomes limitations of specialized ontology languages and tools.
This document discusses informal mathematical proofs that appeal to diagrams and mental models. It proposes criteria for when such proofs can be considered rigorous: (1) the diagram must accurately portray the structure of the mathematical object, (2) the information in the diagram must not be metric, and (3) the inferences must be systematically related to other mathematical practices. The document also discusses how Manders' analysis of proofs in Euclidean geometry may provide a model for understanding contemporary proofs involving diagrams, but notes some challenges for philosophers in analyzing modern mathematical reasoning.
Similar to Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative (20)
Analyzing the Explanatory Power of Bionic Systems With the Minimal Cognitive ...Antonio Lieto
The document discusses bionic systems that connect biological tissues with artificial devices. Two case studies are described:
1) A lamprey experiment where the reticulospinal pathway was replaced with an electromechanical device, allowing investigation of the relationship between input and output.
2) A monkey experiment where neural activity was used to control a cursor, then an artificial actuator. Performance declined initially but improved with feedback, showing plasticity in representing actuator dynamics.
While the artificial components don't directly explain biological mechanisms, they can provide local functional accounts and global insights by allowing investigation of hybrid biological-artificial system functioning.
Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different ...Antonio Lieto
We claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gardenfors [23] for defending the need of a conceptual, intermediate, representation level between
the symbolic and the sub-symbolic one. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and
reasoning in Cognitive Architectures
This document describes a case study using ontological representations and narrative to explore cultural heritage archives. The Labyrinth project uses an ontology modeling narrative elements like stories, actions, characters to allow users to navigate a digital archive. The ontology relates these narrative aspects to archive items. Reasoning over the ontology transfers narrative properties to items, allowing exploration by story or action. A user study will evaluate if this approach supports serendipitous discovery and new learning experiences within cultural heritage archives.
Riga2013 Symposium on Concepts and PerceptionAntonio Lieto
This document discusses the relationship between concepts and perception through the lens of dual process theory. It analyzes different perspectives on the nature of concepts, such as prototype theory and exemplar theory, and proposes that concepts involve both System 1 implicit processes and System 2 explicit processes. System 1 processes are related to perception and involve fast, automatic categorization based on prototypes. In contrast, System 2 processes are slower, more controlled processes like monotonic categorization. The document concludes that understanding the heterogeneous nature of concepts, including both System 1 and System 2 aspects, can provide insight into the complex relationship between concepts and perception.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
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Exposé invité Journées Nationales du GDR GPL 2024
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Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
8.Isolation of pure cultures and preservation of cultures.pdf
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative
1. Cognitive Agents with Commonsense
Antonio Lieto
Università di Torino, Dipartimento di Informatica, IT
ICAR-CNR, Palermo, IT
February 18 2021, iCog seminars, Istituto Italiano di Tecnologia (IIT)
2. Outline
– Knowledge representation and processing in CAs: Open
problems
– Current Solutions (and their problems): Extended Declarative
Memories
– More Constrained Knowledge Processing Models
– A Case Study on Linguistic Categorization: DUAL-PECCS
3. Preamble
– Cognitivist Cognitive Architectures are assumed to be well-
equipped in dealing with aspects concerning knowledge processing
and high-level cognition with respect to the emergentist/
developmental ones.
– Unfortunately there are some problems that limit their role in a
computationally grounded science of the mind.
6. Knowledge Level Analysis
Knowledge Level (Newell, 1982; 1990) = level of analysis and prediction of the
rational behavior of a cognitive agent (based on the assumed availability of the
agent knowledge, in order to pursue its own goals and related actions).
Can we use the models built in Cognitive Architectures as a computational
proxy of the human knowledge processing capabilities?
7. Current Problems at the “Knowledge Level”
CAs are general structures without a corresponding “general”
content (SIZE PROBLEM). Ad hoc/task specific built knowledge.
The knowledge represented and manipulated by such CAs is usually
homogeneous in nature (HOMOGENEITY PROBLEM)
Lieto, A., Lebiere, C., & Oltramari, A. (2018). The knowledge level in cognitive architectures: Current
limitations and possible developments. Cognitive Systems Research, 48, 39-55.
8. SIZE problem
Conceptual knowledge in humans is a huge, variegated and multi-
domain.
To test the architectural mechanisms of memory storage,
retrival, reasoning we should endow our agent with a human-level
knowledge (=> one of Newell’s criteria for a theory of cognition).
Why?
Having a system with huge knowledge poses immediately
computational and cognitive problems concerning the retrieval of
the correct knowledge given a task to solve that are neglected or
hidden under the carpet with toy-knowledge bases.
9. Solutions: Extended Declarative Memories
- Soar terms connected to the linguistic resource WordNet
but:
only some taxonomical relations
between terms
(Derbinsky et al., 2010)
11. Solutions: Extended Declarative Memories
- Such solutions are all available in ACT-R
Ball et al. 2008
Salvucci et al. 2014 (DbPedia)
12. Problems
- All such solutions extends Declarative Memories with symbolic/
ontological semantic representations
- However symbol-like representations encounters problems in
dealing with common-sense knowledge representation and reasoning
(e.g. approximate reasoning is computationally hard in graph-like
structures). (HOMOGENEITY PROBLEM)
13. (lack of) HETEROGENITY problem
Classical vs Commonsense knowledge
Knowledge represented and manipulated by such CAs mainly the so
called “classical” part of conceptual information (that one
representing concepts in terms of necessary and sufficient
conditions).
The so called “common-sense” conceptual components of our
knowledge is largely absent in such computational frameworks.
14. Classical Theory – Ex.
22
TRIANGLE = Polygon with 3 corners and sides
PROBLEM: Common-sense concepts cannot be defined in this way.
There are many theories developed in cognitive science trying to
provide an explanation to the problem to typicality
15. ….
AI and CogSci approaches to Commonsense
reasoning (partial overview)
Semantic Networks
(Collins and Quillians, 1969)
Classical
Theory
Prototype Theory
Rosch (1975)
Frames
(Minsky, 1975)
Scripts
(Shank & Abelson,
1977)
Circumscription
(Mc Carthy, 1980)
Exemplar Theory
Medin and Schaffer (1978)
18. Commonsense reasoning
Concerns all the type of non deductive (or non
monotonic) inference:
- induction
- abduction
- default reasoning
- …
18
19. Commonsense reasoning
Concerns all the type of non deductive (or non
monotònic) inference:
- induction
- abduction
- default reasoning
- …
19
TIPICALITY
20. Prototypes and Prototypical Reasoning
• Categories based on prototypes (Rosh,1975)
• New items are compared to the prototype
atypical
typical
P
21. Ad-hoc Solutions
Use ontologies as frame structures (Misky) or
with “commonsense rules” able to perform
some commonsense inferences
22. Ad-hoc Solutions
Use ontologies as frame structures (à la
Minsky) or with “commonsense rules” able to
perform some commonsense inferences
BIRD ⊑ FLY
23. Ad-hoc Solutions
Use ontologies as frame structures (à la
Minsky) or with “commonsense rules” able to
perform some commonsense inferences
BIRD ⊑ FLY
IF X {Wag Tails, Barks, hasFur}
24. Ad-hoc Solutions
Use ontologies as frame structures (à la
Minsky) or with “commonsense rules” able to
perform some commonsense inferences
BIRD ⊑ FLY
IF X {Wag Tails, Barks, hasFur}
26. Problems
This knowledge engineering approach works
for well-defined narrow domains but it is does
not scale and is not generalizable.
Why? Prototypes and Commonsense
knowledge dynamic and context dependent.
29. Exemplars and Exemplar-based Reasoning
• Categories as composed by a list of exemplars. New
percepts are compared to known exemplars (not to
Prototypes).
30. Conflicting Theories?
• Exemplars theory overcomes the Prototypes (it can
explain so called OLD ITEM EFFECT).
• Still in some situations prototypes are preferred in
categorization tasks.
30
31. Conflicting Theories?
• Exemplars theory overcomes the Prototypes (it can
explain so called OLD ITEM EFFECT).
• Still in some situations prototypes are preferred in
categorization tasks.
Prototypes, Exemplars and other conceptual
representations (for the same concept) can co-exists
and be activated in different contexts (Malt 1989).
31
32. Type 1/Type 2 features
32
ACT-R
(Anderson et
al. 2004)
CLARION (Sun,
2006)
Vector-LIDA
(Franklin et al.
2014)
SOAR (Laird
2012)
Concepts as chunks
(symbolic
structures)
Neural networks +
Symbol Like
representations
High dimensional
vector spaces
Concepts as chunks
(symbolic
structures)
Sub-symbolic and
Bayesian
activation of chunks
Subsymbolic
activation of
conceptual chunks
Similarity based
vectorial activation
Rule-based
activation and firing
of chunks
Prototypes and
Exemplars models
of categorisation
available in
separation
Prototypes and
Exemplars models
of categorisation
NOT available
Prototypes and
Exemplars models
of categorisation
NOT available
Prototypes and
Exemplars models
of categorisation
NOT available
Extended
Declarative Memory
CYC, DBPedia)
Ad hoc or narrow
Knowledge
Ad hoc or narrow
Knowledge
Extended Semantic
Memory with
linguistic resources
(ex. Wordnet)
33. DUAL PECCS: DUAL- Prototype and Exemplars
Conceptual Categorization System
Lieto, Radicioni, Rho (IJCAI 2015, JETAI 2017)
34. 34
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
…
35. 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
36. 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)
37. Heterogeneous Proxytypes in DUAL-PECCS
37
dopting differ-
mbolic perspec-
oded in terms
orks [Quillian,
prototypes can
convex region
mbolic perspec-
concept can, on
atterns of con-
Ns). Similarly,
both symbolic
sed, as well as
emplars can be
mbolic systems,
or as a partic-
inally, also for
t in principle–,
ver, this seems
evels are more
nceptual repre-
tificial systems
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
Figure 1: Heterogeneous representation of the tiger concept
our system includes two main sorts of components, based on
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.
Lieto et al. (2015), A Common-Sense Conceptual Categorization System Integrating
Heterogeneous Proxytypes and the Dual Process of Reasoning, IJCAI, AAAI Press.
38. 38
ng differ-
perspec-
in terms
Quillian,
types can
ex region
perspec-
pt can, on
s of con-
Similarly,
symbolic
s well as
rs can be
systems,
a partic-
also for
inciple–,
is seems
are more
al repre-
systems
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
Figure 1: Heterogeneous representation of the tiger concept
our system includes two main sorts of components, based on
Co-referring representational Structures via Wordnet
Lieto, A., Mensa, E,, Radicioni, D., 2016. A resource-driven approach for anchoring linguistic resources
conceptual spaces. In Conference of the Italian Association for Artificial Intelligence (pp. 435-449). Springer, Cham.
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
41. ACT-R Integration
• “Extended” Declarative
Memory of ACT-R
• Integration of the dual
process base categorisation
processes in ACT-R
41
for a given concept can be represented by adopting differ-
ent computational frameworks: i) from a symbolic perspec-
tive, prototypical representations can be encoded in terms
of frames [Minsky, 1975] or semantic networks [Quillian,
1968]; ii) from a conceptual space perspective, prototypes can
be geometrically represented as centroids of a convex region
(more on this aspect later); iii) from a sub-symbolic perspec-
tive, the prototypical knowledge concerning a concept can, on
the other hand, be represented as reinforced patterns of con-
nections in Artificial Neural Networks (ANNs). Similarly,
for the exemplars-based body of knowledge, both symbolic
and conceptual space representations can be used, as well as
the sub-symbolic paradigm. In particular, exemplars can be
represented as instances of a concept in symbolic systems,
as points in a geometrical conceptual space, or as a partic-
ular (local) pattern of activation in a ANN. Finally, also for
the classical body of knowledge it is –at least in principle–,
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
ACT-R concepts represented as en “empty
chunk” (chunk having no associated information,
except for its WordNet synset ID and a human
readable name), referred to by the external bodies
of knowledge (prototypes and exemplars) acting
like semantic pointers.
42. CLARION Integration
• “Extende
42
for a given concept can be represented by adopting differ-
ent computational frameworks: i) from a symbolic perspec-
tive, prototypical representations can be encoded in terms
of frames [Minsky, 1975] or semantic networks [Quillian,
1968]; ii) from a conceptual space perspective, prototypes can
be geometrically represented as centroids of a convex region
(more on this aspect later); iii) from a sub-symbolic perspec-
tive, the prototypical knowledge concerning a concept can, on
the other hand, be represented as reinforced patterns of con-
nections in Artificial Neural Networks (ANNs). Similarly,
for the exemplars-based body of knowledge, both symbolic
and conceptual space representations can be used, as well as
the sub-symbolic paradigm. In particular, exemplars can be
represented as instances of a concept in symbolic systems,
as points in a geometrical conceptual space, or as a partic-
ular (local) pattern of activation in a ANN. Finally, also for
the classical body of knowledge it is –at least in principle–,
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
• natively “dual process”
• Typicality information (conceptual space
—> implicit NACS layer
• Classical (ontology)—> explicit NACS
The mapping between the sub-symbolic module of
CLARION and the vector-based representations of the
Conceptual Spaces has been favored, since such
architecture also synthesizes the implicit information in
terms of dimensions-values pairs
43. ACT-R, SOAR, CLARION and LIDA Extended Declarative Memories with
DUAL-PECCS
Salvucci et al. 2014 (DbPedia)
46. 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.
112 common sense linguistic descriptions provided by a team of linguists,
philosophers and neuroscientists interested in the neural basis of lexical
processing (FMRI).
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
47. 47
• Two evaluation metrics have been devised:
- Concept Categorization Accuracy: estimating how often the
correct concept has been retrieved;
- Proxyfication Accuracy: how often the correct concept has
been retrieved AND the expected representation has been
retrieved, as well.
Accuracy Metrics
48. 48
• Three sorts of proxyfication errors were committed:
- Ex-Proto, an exemplar is returned in place of a prototype;
- Proto-Ex, we expected a prototype, but a prototype is
returned;
- Ex-Ex, an exemplar is returned differing from the
expected one.
• Three sorts of proxyfication errors were committed:
- Ex-Proto, an exemplar is returned in place of a prototype;
- Proto-Ex, we expected a prototype, but a prototype is
returned;
- Ex-Ex, an exemplar is returned differing from the
expected one.
Proxyfication Error
49. 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 between co-existing
representations in the heterogeneous hypothesis.
50. Upshots and Future direction
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.
Cognitive architectures should be endowed with more
constrained knowledge processing mechanisms to test their
representational and reasoning assumptions (commonsense as
crucial component).
Commonsense could be the “bridge” between perception and
cognition.
Need to find non ad-hoc integration solutions.
The mechanisms showed could influence other components
(e.g. episodic memory & exemplars; affordances & prototypes) in
an integrated architecture.
51. Cognitive Design for Artificial Minds
51
Forthcoming in 2021 !!
Taylor and Francis
Forthcoming in April 2021 !!
Taylor and Francis