1. The document discusses several approaches to cognitive science including connectionism, neural networks, supervised and unsupervised learning, Hebbian learning, the delta rule, backpropagation, and responses to Descartes from Gelernter, Penrose, and Pinker.
2. Connectionism models mental phenomena using interconnected networks of simple units like neural networks. Learning involves adjusting connection weights between neurons.
3. Supervised learning uses input-output pairs to adjust weights to minimize error, while unsupervised learning only uses inputs to find patterns in the data.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
The document discusses cognitive architectures, which are engineering approaches for modeling cognitive systems like humans. It notes that cognitive architectures aim to provide a unified set of mechanisms to explain various cognitive functions like language, problem solving, dreaming, goal-directed behavior, symbol usage, and learning. The document then reviews several specific cognitive architectures, including Soar, ACT-R, LIDA, and 4CAPS. It also discusses challenges in creating cognitive architectures that integrate symbolic and sub-symbolic approaches and can be implemented on neural hardware at large scales.
Cognitive science is the interdisciplinary study of the mind and its processes. It includes psychology, artificial intelligence, neuroscience, linguistics, and other fields. The document provides an overview of the key topics in cognitive science, including knowledge representation, language, learning, thinking, and perception. It also discusses different approaches like symbolic and connectionist computational cognitive science. The goal of cognitive science is to understand how the mind works by studying representations and processes through various methods like computational modeling.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
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.
the transcript of speech at IASDR 2009 conference
[slides available at http://www.slideshare.net/urijoe/paper-presentation-at-iasdr-2009-seoul-south-korea]
This document discusses how combining probabilistic logical inference (PLN) with a nonlinear dynamical attention allocation system (ECAN) can help address the problem of combinatorial explosion in inference. It presents a simple example using a noisy version of the "smokes" problem where ECAN guides PLN's inference by focusing attention on surprising conclusions, allowing meaningful conclusions to be drawn with fewer inference steps. This demonstrates a cognitive synergy between logical reasoning and attention allocation that is hypothesized to be broadly valuable for artificial general intelligence.
1. The document discusses several approaches to cognitive science including connectionism, neural networks, supervised and unsupervised learning, Hebbian learning, the delta rule, backpropagation, and responses to Descartes from Gelernter, Penrose, and Pinker.
2. Connectionism models mental phenomena using interconnected networks of simple units like neural networks. Learning involves adjusting connection weights between neurons.
3. Supervised learning uses input-output pairs to adjust weights to minimize error, while unsupervised learning only uses inputs to find patterns in the data.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
The document discusses cognitive architectures, which are engineering approaches for modeling cognitive systems like humans. It notes that cognitive architectures aim to provide a unified set of mechanisms to explain various cognitive functions like language, problem solving, dreaming, goal-directed behavior, symbol usage, and learning. The document then reviews several specific cognitive architectures, including Soar, ACT-R, LIDA, and 4CAPS. It also discusses challenges in creating cognitive architectures that integrate symbolic and sub-symbolic approaches and can be implemented on neural hardware at large scales.
Cognitive science is the interdisciplinary study of the mind and its processes. It includes psychology, artificial intelligence, neuroscience, linguistics, and other fields. The document provides an overview of the key topics in cognitive science, including knowledge representation, language, learning, thinking, and perception. It also discusses different approaches like symbolic and connectionist computational cognitive science. The goal of cognitive science is to understand how the mind works by studying representations and processes through various methods like computational modeling.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
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.
the transcript of speech at IASDR 2009 conference
[slides available at http://www.slideshare.net/urijoe/paper-presentation-at-iasdr-2009-seoul-south-korea]
This document discusses how combining probabilistic logical inference (PLN) with a nonlinear dynamical attention allocation system (ECAN) can help address the problem of combinatorial explosion in inference. It presents a simple example using a noisy version of the "smokes" problem where ECAN guides PLN's inference by focusing attention on surprising conclusions, allowing meaningful conclusions to be drawn with fewer inference steps. This demonstrates a cognitive synergy between logical reasoning and attention allocation that is hypothesized to be broadly valuable for artificial general intelligence.
This document provides an introduction to soft computing. It discusses intelligent systems and how traditional approaches use mathematical models and rule-based systems. Soft computing aims to mimic human reasoning using fuzzy systems, neural networks, evolutionary computing, and probabilistic reasoning. Soft computing is tolerant of imprecision, uncertainty, partial truths, and approximations. It has advantages over hard computing by being closer to human thinking and using linguistic models that are simple, comprehensible, and fast. Soft computing has become widely used with over 24,000 publications to date.
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 knowledge representation in cognitive psychology. It defines knowledge and describes two main types: declarative and procedural knowledge. Declarative knowledge refers to static facts and information stored in memory, while procedural knowledge involves skills and how to perform tasks or activities. The document also explains several methods for representing declarative knowledge, including concepts and schemas, frames, and semantic networks. Frames organize knowledge into attribute-value pairs, while semantic networks use a graph structure to represent relationships between concepts. Overall, the document provides an overview of knowledge representation and different models for encoding declarative and procedural information.
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition, and machine vision. The document discusses what intelligence and artificial intelligence are, provides definitions and examples of artificial intelligence, and explains how artificial intelligence works through machine learning algorithms. It also covers the goals, history, and advantages of artificial intelligence.
Knowledge representation is a field of artificial intelligence that represents information about the world in a way that a computer system can understand to perform complex tasks. It simplifies complex systems through modeling human psychology and problem-solving. Examples of knowledge representation include semantic nets, frames, rules, and ontologies. Knowledge representation allows for automated reasoning about represented knowledge and asserting new knowledge. While first-order logic provides powerful and compact representation, it lacks ease of use and practical implementation for real-world problems. Effective knowledge representation requires balancing expressive power with practical considerations like execution efficiency.
Cognitive Architectures Comparision based on perceptual processingSumitava Mukherjee
The document compares several cognitive architectures - ACT-R/PM, SOAR, EPIC, CHREST, and ICARUS - in terms of their approaches to perceptual processing. It analyzes factors like whether initial perceptual information needs to be programmed or learned, the modularity of perception, how expectations are handled, and the granularity of visual representations. While the architectures include some perceptual abilities, the document argues they need to more fully incorporate object-level perception, depth perception, scene perception based on distributed attention theories, and the effects of emotion on perception and attention. More learning from experience is also needed to better ground cognition in perception.
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.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
The document discusses creating an "Educational Futures Evidence Hub" to engage academics, practitioners, enterprises, and policymakers through a dynamic web presence. It describes the Thematic Research Network and its goal of using collective intelligence and open educational resources to better understand the impact of emerging technologies on education. The document raises questions about how to augment systems' ability to sense, respond to, and shape their environment through the lenses of complex adaptive systems, resilience, sensemaking, and human-computer interaction.
7. knowledge acquisition, representation and organization 8. semantic network...AhL'Dn Daliva
This document discusses knowledge acquisition, representation, and organization. It describes the two types of knowledge - declarative and procedural - and five guidelines for knowledge acquisition. It also discusses theories of knowledge representation including rule-based production models, distributed networks, and propositional models. A key point is that semantic networks can be used to represent knowledge as a system of interconnected concepts. The document also discusses long-term memory and its two types - episodic and semantic memory. It describes cognitive semantic networks and models by Collins and Quillian as well as schema theory. Concept maps are discussed as a way to visualize relationships between concepts.
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.
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.
Dual coding theory proposes that there are two separate channels for processing information - a verbal system that handles language and a nonverbal system specialized for visual and spatial processing. Information can be coded verbally as words or nonverbally as mental images. These coding systems operate independently but are interconnected, allowing information to be represented and retrieved through both verbal and nonverbal codes. Dual coding theory suggests that combining words and images leads to better memory than relying on just one code alone.
This document discusses an interactive storytelling system that integrates natural language processing with emotional planning to generate stories. The system uses an emotional planner based on HSP planning that alters characters' beliefs and emotional states. Each character is driven by its own planner and can affect other characters' feelings. The system was tested using a story adapted from Madame Bovary with characters' feelings playing a central role. User input through natural language can update characters' beliefs and emotions and indirectly influence the story.
The document discusses the cognitive science perspective on the origins of mathematical ideas. It argues that mathematics arises from human cognition and ideas, which are grounded in sensory-motor experience. Research shows infants have innate abilities to discriminate quantities and perform basic arithmetic. The brain regions involved in these abilities are the inferior parietal cortex and areas linked to language processing like the supramarginal gyrus. Conceptual metaphors imported from sensory experiences may provide a bridge between perception, language and mathematical reasoning. The nature of mathematical ideas can therefore only be understood through empirical study of human cognition.
Computational imagination aims to model human imagination by creating artificial agents with intelligence, emotions, and imagination. Imagination is a process of forming semantically linked mental images influenced by perceptions, emotions, context, and prior knowledge. It can be formally represented using visual and linguistic means. Applications could include helping people learn from experience, assisting motor skill learning, aiding older adults' memory, better predicting behavior, and disaster preparation.
Neuro-cognitive and psychological linguistics present important area of multidisciplinary research.
In this paper we have described some possible applications of mathematical methods to neuro-cognitive linguistics. In neuro-cognitive study of language, neural architecture and neuropsychological mechanism of verbal cognition are basis of a vector–based modeling. A comparison of human mental space to a vector space is an effective way of analyzing of human semantic vocabulary, mental representations and rules of clustering and mapping in typologically different languages.
Euclidean and non-Euclidean spaces can be applied for a description of human semantic vocabulary and high order structures reflecting internal and external features of object and action (event). Vector analysis of word meaning and basic syntax structures offers new methodological opportunities to interpret effect of semantic and pragmatic forces at morphology and syntax levels.
Non-linear and metaphoric transformations present specific complex phenomenon to be described in 3D and other N-dimensional spaces in the framework of quantum semantics.
Keywords: Mental mapping, human mental lexicon, embodied and symbolic cognition, verbal cognition, semantic space, scalar, vector space, mental transformation, semantic gravity.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of an introductory course on Artificial Intelligence. It discusses the learning outcomes, which include gaining knowledge of core AI concepts like search, game playing, knowledge representation, planning and machine learning. It emphasizes using Python to construct simple AI systems and developing transferable problem solving skills. The document outlines that students are expected to attend lectures, supplement with textbook reading, and use references. It also gives a high-level overview of the different perspectives and definitions of what constitutes Artificial Intelligence.
This document provides an introduction to soft computing. It discusses intelligent systems and how traditional approaches use mathematical models and rule-based systems. Soft computing aims to mimic human reasoning using fuzzy systems, neural networks, evolutionary computing, and probabilistic reasoning. Soft computing is tolerant of imprecision, uncertainty, partial truths, and approximations. It has advantages over hard computing by being closer to human thinking and using linguistic models that are simple, comprehensible, and fast. Soft computing has become widely used with over 24,000 publications to date.
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 knowledge representation in cognitive psychology. It defines knowledge and describes two main types: declarative and procedural knowledge. Declarative knowledge refers to static facts and information stored in memory, while procedural knowledge involves skills and how to perform tasks or activities. The document also explains several methods for representing declarative knowledge, including concepts and schemas, frames, and semantic networks. Frames organize knowledge into attribute-value pairs, while semantic networks use a graph structure to represent relationships between concepts. Overall, the document provides an overview of knowledge representation and different models for encoding declarative and procedural information.
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition, and machine vision. The document discusses what intelligence and artificial intelligence are, provides definitions and examples of artificial intelligence, and explains how artificial intelligence works through machine learning algorithms. It also covers the goals, history, and advantages of artificial intelligence.
Knowledge representation is a field of artificial intelligence that represents information about the world in a way that a computer system can understand to perform complex tasks. It simplifies complex systems through modeling human psychology and problem-solving. Examples of knowledge representation include semantic nets, frames, rules, and ontologies. Knowledge representation allows for automated reasoning about represented knowledge and asserting new knowledge. While first-order logic provides powerful and compact representation, it lacks ease of use and practical implementation for real-world problems. Effective knowledge representation requires balancing expressive power with practical considerations like execution efficiency.
Cognitive Architectures Comparision based on perceptual processingSumitava Mukherjee
The document compares several cognitive architectures - ACT-R/PM, SOAR, EPIC, CHREST, and ICARUS - in terms of their approaches to perceptual processing. It analyzes factors like whether initial perceptual information needs to be programmed or learned, the modularity of perception, how expectations are handled, and the granularity of visual representations. While the architectures include some perceptual abilities, the document argues they need to more fully incorporate object-level perception, depth perception, scene perception based on distributed attention theories, and the effects of emotion on perception and attention. More learning from experience is also needed to better ground cognition in perception.
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.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
The document discusses creating an "Educational Futures Evidence Hub" to engage academics, practitioners, enterprises, and policymakers through a dynamic web presence. It describes the Thematic Research Network and its goal of using collective intelligence and open educational resources to better understand the impact of emerging technologies on education. The document raises questions about how to augment systems' ability to sense, respond to, and shape their environment through the lenses of complex adaptive systems, resilience, sensemaking, and human-computer interaction.
7. knowledge acquisition, representation and organization 8. semantic network...AhL'Dn Daliva
This document discusses knowledge acquisition, representation, and organization. It describes the two types of knowledge - declarative and procedural - and five guidelines for knowledge acquisition. It also discusses theories of knowledge representation including rule-based production models, distributed networks, and propositional models. A key point is that semantic networks can be used to represent knowledge as a system of interconnected concepts. The document also discusses long-term memory and its two types - episodic and semantic memory. It describes cognitive semantic networks and models by Collins and Quillian as well as schema theory. Concept maps are discussed as a way to visualize relationships between concepts.
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.
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.
Dual coding theory proposes that there are two separate channels for processing information - a verbal system that handles language and a nonverbal system specialized for visual and spatial processing. Information can be coded verbally as words or nonverbally as mental images. These coding systems operate independently but are interconnected, allowing information to be represented and retrieved through both verbal and nonverbal codes. Dual coding theory suggests that combining words and images leads to better memory than relying on just one code alone.
This document discusses an interactive storytelling system that integrates natural language processing with emotional planning to generate stories. The system uses an emotional planner based on HSP planning that alters characters' beliefs and emotional states. Each character is driven by its own planner and can affect other characters' feelings. The system was tested using a story adapted from Madame Bovary with characters' feelings playing a central role. User input through natural language can update characters' beliefs and emotions and indirectly influence the story.
The document discusses the cognitive science perspective on the origins of mathematical ideas. It argues that mathematics arises from human cognition and ideas, which are grounded in sensory-motor experience. Research shows infants have innate abilities to discriminate quantities and perform basic arithmetic. The brain regions involved in these abilities are the inferior parietal cortex and areas linked to language processing like the supramarginal gyrus. Conceptual metaphors imported from sensory experiences may provide a bridge between perception, language and mathematical reasoning. The nature of mathematical ideas can therefore only be understood through empirical study of human cognition.
Computational imagination aims to model human imagination by creating artificial agents with intelligence, emotions, and imagination. Imagination is a process of forming semantically linked mental images influenced by perceptions, emotions, context, and prior knowledge. It can be formally represented using visual and linguistic means. Applications could include helping people learn from experience, assisting motor skill learning, aiding older adults' memory, better predicting behavior, and disaster preparation.
Neuro-cognitive and psychological linguistics present important area of multidisciplinary research.
In this paper we have described some possible applications of mathematical methods to neuro-cognitive linguistics. In neuro-cognitive study of language, neural architecture and neuropsychological mechanism of verbal cognition are basis of a vector–based modeling. A comparison of human mental space to a vector space is an effective way of analyzing of human semantic vocabulary, mental representations and rules of clustering and mapping in typologically different languages.
Euclidean and non-Euclidean spaces can be applied for a description of human semantic vocabulary and high order structures reflecting internal and external features of object and action (event). Vector analysis of word meaning and basic syntax structures offers new methodological opportunities to interpret effect of semantic and pragmatic forces at morphology and syntax levels.
Non-linear and metaphoric transformations present specific complex phenomenon to be described in 3D and other N-dimensional spaces in the framework of quantum semantics.
Keywords: Mental mapping, human mental lexicon, embodied and symbolic cognition, verbal cognition, semantic space, scalar, vector space, mental transformation, semantic gravity.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of an introductory course on Artificial Intelligence. It discusses the learning outcomes, which include gaining knowledge of core AI concepts like search, game playing, knowledge representation, planning and machine learning. It emphasizes using Python to construct simple AI systems and developing transferable problem solving skills. The document outlines that students are expected to attend lectures, supplement with textbook reading, and use references. It also gives a high-level overview of the different perspectives and definitions of what constitutes Artificial Intelligence.
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will also develop intellectual skills to synthesize solutions and evaluate alternatives, and practical skills to use Prolog and construct simple AI systems. The course will cover topics in search, knowledge representation, planning, machine learning, logic, expert systems, robotics, natural language processing, and their dependencies. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge bases, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. Practical skills include using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge bases, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. Practical skills include using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will develop skills in using languages like Prolog to construct simple AI systems and solve problems. The course covers areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge bases, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. Practical skills include using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will also develop intellectual skills to synthesize solutions and evaluate alternatives, and practical skills to use Prolog and construct simple AI systems. The course will cover topics in search, knowledge representation, planning, machine learning, logic, expert systems, robotics, natural language processing, and their dependencies. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. They will also gain practical skills using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
901470_Chap1.ppt about to Artificial Intellgencechougulesup79
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will develop skills in using languages like Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will develop skills in using languages like Prolog to construct simple AI systems and solve problems. The course covers areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
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This document provides an overview of an introductory course on artificial intelligence. The course aims to teach students basic concepts in AI including search, game playing, knowledge-based systems, planning, and machine learning. Students will learn a declarative programming language and how to construct simple AI systems. By the end of the course, students should have knowledge and skills in AI principles, be able to synthesize solutions to AI tasks, and critically evaluate alternatives. The document outlines the main topics that will be covered in the course.
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doc. dr Milan Zdravković. Mašinski fakultet u Nišu, Inženjerski menadžment, studijski profil Industrijski menadžment, master studije, 1.godina
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Pristup online kursu: http://ekursevi.masfak.ni.ac.rs:9000/courses/course-v1:MEF+UPRO+2019-20_S2/about
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doc. dr Milan Zdravković. Mašinski fakultet u Nišu, Inženjerski menadžment, studijski profil Industrijski menadžment, master studije, 1.godina
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1. A myth or a vision for
interoperability: can systems
communicate like humans do?
dr Milan Zdravković
Laboratory for Intelligent Production Systems (LIPS)
Faculty of Mechanical Engineering in Niš, University of Niš,
Serbia
Seminar - Interoperability challenges and needs: When Research meets Industry, 3rd June
2013, CRP Henri Tudor, Luxembourg
2. Statement of the problem
• Motivation
– In the future IoT, every “thing” will be a system
• More complexity, less previous agreements and assumptions
• Can one system operate based on the message(s) of
the arbitrary content, sent by the (an)other
(unknown) system(s)?
– It is the problem of systems interoperability, not data,
enterprise, etc..
– How to represent that content and how to reason based
on that content?
Artificial intelligence
3. Illusion of / artificial intelligence
• Turing test (Turing, 1950)
– Test of a machine's ability to
exhibit intelligent behavior
equivalent to, or
indistinguishable from, that of
an actual human
– Turing reduced the problem of
defining intelligence to a simple
conversation
• Example: ELIZA
– Examines users’ comments for
keywords
PARRY
4. If more specific context for illusion is
provided, odds are getting better
• PARRY (1972) attempted to model the
behavior of a paranoid schizophrenic
• Easily passed the Turing test (evaluated by
psychiatrists)
– correct identification only 48 per cent of the time
- a figure consistent with random guessing
Chinese room experiment
5. Chinese room experiment
• Proves that Turing test could
not be used to determine if a
machine can think
• The experiment is the
centerpiece of Searle's
Chinese room argument
which holds that a program
cannot give a computer a
"mind", "understanding" or
"consciousness", regardless
of how intelligently it may
make it behave
Chinese room argument
6. Chinese room argument
• Axioms
– (A1) "Programs are formal (syntactic)."
– (A2) "Minds have mental contents (semantics)."
– (A3) "Syntax by itself is neither constitutive of nor
sufficient for semantics.“
• Problem: What experiment show is that passing a Turing test is
possible without understanding. It does not show that its not
possible to reconstruct or interpret semantics based on the syntax
• Conclusion
– (C1) Programs are neither constitutive of nor sufficient for
minds.
Do systems need to “understand”?
7. OK, systems can(not) be intelligent
(can(not) understand), but is that really
important?
• Turing test is explicitly anthropomorphic
– If our ultimate goal is to create machines that
are more intelligent than people, why should we insist
that our machines must closely resemble people?
– Russell and Norvig: “the goal of aeronautical engineering is
not to make machines that fly exactly like pigeons because
they need to fool other pigeons”
• For example, DL is somewhat close to the knowledge
representation in our minds. But, could it be possible that
knowledge may be represented (or reasoning implemented) in
some other way, by using other kinds of formalisms (not yet
existing)?
Functionalism
8. Functionalism (Putnam, 1960)
• Mental states (beliefs, desires, being in pain, etc.) are
constituted solely by their functional role - that is,
they are causal relations to other mental states,
sensory inputs, and behavioral outputs
• Mental states are able to be manifested in various
systems, even perhaps computers, so long as the
system performs the appropriate functions
– Mental states can be sufficiently explained without taking
into account the underlying physical medium
• Computational theory of mind (Putnam, 1961)
– the mind is a machine that derives output representations
of the world from input representations in a deterministic
(non-random) and formal (non-semantic) way
Reverse Chinese Room argument
9. Reverse Chinese Room argument
• There may exist a system which when
provided with detailed instructions on how to
interpret “sensory inputs”, could be able to
produce corresponding (reasonable)
“behavioral outputs”, or a “mental state”
whatsoever.
Sensory inputs
? Behavioral outputs
Definition of interoperability
10. My favorite definition (of MANY) of
interoperability
• ISO/IEC 2382 defines interoperability as the
• “capability (of the agent) to communicate,
execute programs, or transfer data among
various functional units in a manner that
requires the user (agent) to have little or no
knowledge of the unique characteristics of
those units”.
Sensation
11. Sensation
• Senses are physiological cap
acities of organisms that
provide data for perception
• However, perception and
sensation cannot be
considered in isolation,
because of the filtering
(selection), organizing
(grouping, categorization)
even interpretation
processes
– organizing various stimuli into
more meaningful patterns
Checker shadow illusion
12.
13. Perception
• Brain's perceptual systems
actively and pre-consciously
attempt to make sense of their
input
Distal stimulus
(object)
Input energy Sense
Transduction
Proximal stimulus
(object)
Pattern of
neural activity
Processing
Percept
Mental recreation of
the distal stimulus
Perceptual set
14. Perceptual set (expectancy)
• Predisposition to perceive things
in a certain way
– Experience, expectation,
motivation
• Sensations are, by themselves,
unable to provide a unique
description of the world
– Perception is both bottom-up
(senses) and top-down (perceptual
set) process
• Perceptual bias (negative)
– Epistemic commitment
– For example, referee decisions in a
football game
sael
seal
sail
Grouping
Interpretation of non-word
by using different perceptual
sets
15. Could perception be formalized?
Gestalt laws of grouping (1923)
• Laws that, hypothetically, allow us to predict
the interpretation of sensation
– We tend to order our experience in a manner that
is regular, orderly, symmetric, and simple.
– A major aspect of Gestalt psychology is that it
implies that the mind understands external stimuli
as whole rather than the sum of their parts
• Grouping by proximity, similarity, complementarity
(closure), symmetry, continuity, etc.
Cognition
16. Cognition
• How we know the world
– The term "cognition" refers to all processes by
which the sensory input is transformed, reduced,
elaborated, stored, recovered, and used.
• Include processes, such as memory,
association, concept formation, pattern
recognition, attention, perception, problem
solving, mental imaginery,..
Concept learning
17. Concept learning
• Bruner (1967): "the search for and listing of
attributes that can be used to distinguish
exemplars from non exemplars of various
categories.“
– Trial-and-error
– Based on applied perception rules (not only
identification, but also assumption)
• Explanation-based theory of concept learning
– Mind observes or receives the qualities of a thing,
– Then, it forms a concept which possesses and is
identified by those qualities
– Derived from theory of progressive generalizing
(1986)
• the mind separates information that applies to
more than one thing and enters it into a broader
description of a category of things. This is done by
identifying sufficient conditions for something to fit
in a category
Intensional conceptualization
18. Intensional conceptualization
• Logical positivists: meaning is nothing more or less
than the truth conditions it involves.
• Here, the meaning is explained by using the
references to the actual existing (possibly also
logically explained) things in the world
– By using not only necessary but also sufficient conditions
• The process of the representation of such meanings
is called intensional conceptualization.
Meaning in linguistics
19. Meaning in linguistics
• Meaning is what the sender expresses,
communicates or conveys in its message to the
receiver (or observer) and what the receiver infers
from the current context (Akmajian et al, 1995)
– Different contexts -> different interpretations
– Linguistics context
• how meaning is understood, without relying on intent and
assumptions
• Depend on the expressivity of vocabulary and level of abstraction
– Situational context
• refers to non-linguistic factors which affect the meaning of the
message
Definition of systems interoperability
20. Formalized systems interoperability
(based on Sowa, 2000)
data(p) ∧ system(S) ∧ system(R) ∧
interoperable(S,R) ⇒
∀p
(
(transmitted-from(p,S) ∧ transmitted-to(p,R))
∧
∀q(statement-of(q,S) ∧ p⇒q) ∃q’(statement-
of(q’,R) ∧ p⇒q’ ∧ q’⇔q)
)
Summary of human communication process
21. Human communication as a raw model for
interoperability
SensationSensationPerceptionPerception
CognitionCognition ArticulationArticulation
Providing meaning to
various sensations
In contexts of
perceptual
sets:
motivation,
expectations,
experience,
culture, etc.
Gaining
knowledge and
comprehension
from the
sensations
Storage, reasoning,
problem solving, imagining,
concept learning
Stimulus
sensory energy
Articulating
response
Recipients,
language, means
22. SensationPerception
Cognition Articulation
∃R(system(R))
Requirements for interoperability
Sensation Perception
CognitionArticulation
• Sensation
– “Ask” & “Tell” interface
• Perception
– Grouping, categorization and
selection Laws: Semantic
matching and reasoning
– Perceptual set
• Explicit knowledge
(ontologies)
• Motivation?
Web
servicesOntologies
Query
processing
Semantic
matchingReasoner
• Cognition
– Triple store
– Formalized business rules
– Rules-enabled reasoning
(generalization and
specialization)
– Assertion of new
knowledge
Ontologies
Mappings ∃S(system(S))
∀p (
(transmitted-from(p,S) transmitted-to(p,R))∧ ∧
∀q(statement-of(q,S) p q)∧ ⇒
∃q’(statement-of(q’,R) p q’ q’ q)∧ ⇒ ∧ ⇔
) ⇒ interoperable(S,R)
Implementation of interoperable systems
23. Cn
C1
C2
Implementing interoperable systems
OL1
OD1
OL2
ML1D1
ML2D1
MO1O2≡f(ML1D1 , ML2D1)
S1
S2
MLnD1
Sn
OLn
MO1On≡f(ML1D1 , MLnD1)
OD2
Si
OLi
MLiD2
MD1D2
MO1Oi≡f(ML1D1 , MD1D2, MLiD2)
• S1-Sn – Enterprise Information
Systems
• OL1-OL2 – Local ontologies
• OD1,2 – Domain ontologies
• MLiDi – Mappings between local
and domain ontologies
“Human” ontology
24. What makes the good (“human”)
ontology (1/2) ?
• Well-defined scope
– Provided context for communication, by set of
competency questions
– Think of ontology as a perceptual set
• In situational (motivation) and linguistics (expressivity, related to
domain(s)) context
– More implicit, the better ?
• Intensional approach to conceptualization
– Remember explanation-based theory of concept learning?
• Epistemic commitment
– Obligation to uphold the factual truth of a given
proposition and to provide reasons for one’s belief in that
proposition, irrespectively of the context
25. What makes the good (“human”)
ontology (2/2) ?
• Taxonomy
– Referring to “internal” or “external” concepts
– Remember progressive generalization?
• No ontology is an island
– Mappings with the concepts of other ontologies in
horizontal (expressivity) and vertical (level of abstraction)
direction
• Meta-ontologies
– Complement the DL expressivity with new representation
and inference methodologies and strategies
“Human” ontology continuum
27. Some future challenges
• Methodology issues
– Semantic vs. semantically-facilitated
interoperability
– Avoiding Yet-Another-Ontology (YAO) syndrome
– Is expressivity of DL sufficient to facilitate efficient
and effective reasoning and/or semantic
matching?
• Technical issues
– How to make systems and local ontologies work
together?
28. Thank you for your attention
dr Milan Zdravković
milan.zdravkovic@gmail.com
http://www.masfak.ni.ac.rs/milan.zdravkovic
Laboratory for Intelligent Production Systems (LIPS)
Faculty of Mechanical Engineering in Niš, University of Niš,
Serbia