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This document summarizes key concepts from a chapter about intelligence:
- It describes different theories of intelligence including general intelligence (g) proposed by Spearman, multiple intelligences proposed by Thurstone and Gardner, and emotional intelligence.
- It discusses intelligence testing and controversies, such as whether intelligence is a single ability or made up of multiple abilities. It also discusses research locating intelligence in the brain.
- The document summarizes different views of intelligence including general intelligence (g), multiple intelligences, emotional intelligence, and intelligence as proposed by theorists like Spearman, Thurstone, Gardner, and Sternberg.
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1) It discusses the origins of LCS from Holland's vision of adaptive systems and genetic algorithms. Early implementations included Holland and Reitman's CS-1 system.
2) It describes two main types of LCS - Michigan-style systems which use a population of rules and reinforcement learning, and Pittsburgh-style systems which use a genetic algorithm on whole individuals.
3) It explains that Michigan-style LCS represent knowledge as a population of condition-action rules, use reinforcement learning to apportion credit, and apply a genetic algorithm to evolve the rule population over time.
This document discusses symbolic reasoning under uncertainty. It introduces monotonic reasoning, where conclusions remain valid even when new information is added, and non-monotonic reasoning, where conclusions can be invalidated by new information. For non-monotonic reasoning, it provides an example where concluding a bird can fly is invalidated by learning the bird is a penguin. The document is presented by Prof. Khushali B Kathiriya and outlines introduction to monotonic reasoning, introduction to non-monotonic reasoning, and an example of non-monotonic reasoning logic.
This document summarizes a lecture on reinforcement learning and the Q-learning algorithm. Q-learning is a temporal difference learning method that allows an agent to learn optimal policies without needing a model of the environment's dynamics. The algorithm works by learning an action-value function (Q-function) that directly approximates the optimal Q-function through Q-backups without requiring a model of the environment. Pseudocode is provided for the basic Q-learning algorithm. Examples are also given showing how Q-learning can be used to learn an optimal policy for navigating a maze.
Why sevenSolutions? In this presentation I have tried to answer this, and analyze the significance of seven in Business Analysis, Education and on Life!
Understanding arguments, reasoning and hypothesesMaria Rosala
As researchers working in government, influencing service design, we need to know that our research is methodologically sound, our research findings are grounded in empirical data and our recommendations are logically derived.
'Understanding arguments, reasoning and hypotheses' is the first in a series of 5 short courses, covering introduction courses to various aspects of methodology in research, from the use of grounded theory in discovery research, to hypothesis testing and sampling in more experimental research.
In this course, you'll learn:
About arguments
- what we mean by an argument
- how to identify a valid/invalid argument
- what we mean by premises
- what validity and soundness of arguments mean
About reasoning
- what is deductive reasoning and where do we use it
- what is inductive reasoning and where do we use it
- what is abductive reasoning and where do we use it
About hypotheses
- what is a hypotheses and a null hypothesis
- how do we test them
The document discusses artificial intelligence and human thinking. It proposes the Abductive Logic Programming (ALP) agent model as a unifying framework for both. ALP clausal logic can serve as the Language of Thought (LOT), representing a private, language-like representation in the mind. Additionally, ALP clausal logic can function as a connectionist model of the mind by representing concepts and relationships between concepts.
The document discusses how people's perceptions drive their actions, as people interpret situations and directives based on their own understanding. It notes that people communicate carefully based on goals of survival and avoiding problems. Managing perceptions is important to expedite results and avoid surprises, as the perceptions people have may not be what was originally intended.
This document summarizes key concepts from a chapter about intelligence:
- It describes different theories of intelligence including general intelligence (g) proposed by Spearman, multiple intelligences proposed by Thurstone and Gardner, and emotional intelligence.
- It discusses intelligence testing and controversies, such as whether intelligence is a single ability or made up of multiple abilities. It also discusses research locating intelligence in the brain.
- The document summarizes different views of intelligence including general intelligence (g), multiple intelligences, emotional intelligence, and intelligence as proposed by theorists like Spearman, Thurstone, Gardner, and Sternberg.
This document provides an introduction and overview of learning classifier systems (LCS), including:
1) It discusses the origins of LCS from Holland's vision of adaptive systems and genetic algorithms. Early implementations included Holland and Reitman's CS-1 system.
2) It describes two main types of LCS - Michigan-style systems which use a population of rules and reinforcement learning, and Pittsburgh-style systems which use a genetic algorithm on whole individuals.
3) It explains that Michigan-style LCS represent knowledge as a population of condition-action rules, use reinforcement learning to apportion credit, and apply a genetic algorithm to evolve the rule population over time.
This document discusses symbolic reasoning under uncertainty. It introduces monotonic reasoning, where conclusions remain valid even when new information is added, and non-monotonic reasoning, where conclusions can be invalidated by new information. For non-monotonic reasoning, it provides an example where concluding a bird can fly is invalidated by learning the bird is a penguin. The document is presented by Prof. Khushali B Kathiriya and outlines introduction to monotonic reasoning, introduction to non-monotonic reasoning, and an example of non-monotonic reasoning logic.
This document summarizes a lecture on reinforcement learning and the Q-learning algorithm. Q-learning is a temporal difference learning method that allows an agent to learn optimal policies without needing a model of the environment's dynamics. The algorithm works by learning an action-value function (Q-function) that directly approximates the optimal Q-function through Q-backups without requiring a model of the environment. Pseudocode is provided for the basic Q-learning algorithm. Examples are also given showing how Q-learning can be used to learn an optimal policy for navigating a maze.
Why sevenSolutions? In this presentation I have tried to answer this, and analyze the significance of seven in Business Analysis, Education and on Life!
Understanding arguments, reasoning and hypothesesMaria Rosala
As researchers working in government, influencing service design, we need to know that our research is methodologically sound, our research findings are grounded in empirical data and our recommendations are logically derived.
'Understanding arguments, reasoning and hypotheses' is the first in a series of 5 short courses, covering introduction courses to various aspects of methodology in research, from the use of grounded theory in discovery research, to hypothesis testing and sampling in more experimental research.
In this course, you'll learn:
About arguments
- what we mean by an argument
- how to identify a valid/invalid argument
- what we mean by premises
- what validity and soundness of arguments mean
About reasoning
- what is deductive reasoning and where do we use it
- what is inductive reasoning and where do we use it
- what is abductive reasoning and where do we use it
About hypotheses
- what is a hypotheses and a null hypothesis
- how do we test them
The document discusses artificial intelligence and human thinking. It proposes the Abductive Logic Programming (ALP) agent model as a unifying framework for both. ALP clausal logic can serve as the Language of Thought (LOT), representing a private, language-like representation in the mind. Additionally, ALP clausal logic can function as a connectionist model of the mind by representing concepts and relationships between concepts.
The document discusses the IEEE Signal Processing Society and the Greek signal processing community. It provides a brief history of signal processing and its influences from other fields. It notes the ubiquity of signals and signal processing. It then summarizes the current state and challenges facing the IEEE Signal Processing Society. It provides details on the local Greek SPS chapter, including its size, activities, and plans for coordinating with the broader Greek signal processing community. These plans include making the Greek SP Jam a regular event and establishing workshops, summer schools, lectures, decentralized events, and awards.
Professor Professor Joseph Sifakis gave a lecture on From Programs to Systems – Building a Smarter World in the Distinguished Lecturer Series - Leon The Mathematician.
Ahmed K. Elmagarmid (IEEE Fellow and ACM Distinguished Scientist) gave a lecture on Data Quality: Not Your Typical Database Problem in the Distinguished Lecturer Series - Leon The Mathematician.
Nicholas Kalouptsidis, Professor, National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Nonlinear Communications: Achievable Rates, Estimation, and Decoding
Professor Ivica Crnkovic gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
This document discusses compressive spectral image sensing and optimization. It introduces compressive spectral imaging (CASSI) which uses coded apertures to sense a datacube with only N^2 measurements rather than the traditional N x N x L measurements. Coded apertures can be optimized for sensing and reconstruction performance as well as spectral selectivity and image classification. New families of coded apertures include boolean, spectrally selective, super-resolution, and colored apertures.
This document summarizes a talk on influence propagation in large graphs. It discusses theorems and algorithms related to modeling the spread of information, viruses, and diseases over networks. The document begins by motivating the importance of understanding dynamical processes over networks through examples related to epidemiology, viral marketing, cybersecurity, and more. It then outlines threshold results for epidemic models on static graphs that depend on the largest eigenvalue of the graph's adjacency matrix and properties of the propagation model. The talk discusses proofs of these results and also covers extensions to dynamic graphs and competing viruses. Finally, it discusses algorithms for determining who to immunize to control outbreaks.
Professor Maria Petrou gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Professor Xin Yao gave a lecture on "Co-evolution, games, and social behaviors" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/G7MdD
Georgios Giannakis, Professor and ADC Chair in Wireless Telecommunications, University of Minnesota, Department of Electrical & Computer Engineering (IEEE/EURASIP Fellow, IEEE SPS DL), Sparsity Control for Robustness and Social Data Analysis
Professor Ismail Toroslu gave a lecture on "Web Usage Mining and Using Ontology for Capturing Web Usage Semantic" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Constantine Kotropoulos, Associate Professor, Aristotle University of Thessaloniki, Department of Informatics, Sparse and Low Rank Representations in Music Signal Analysis
This document discusses using model checking techniques for safety critical systems at NASA. It begins by introducing model checking and how it can be used to verify that a program or model satisfies a given property. It then discusses challenges like the state explosion problem and presents compositional verification as a way to address this by breaking the verification task into checking smaller components. The document provides several examples of applying these techniques to real NASA systems like rovers and spacecraft software.
Ioannis Pitas, Professor, Aristotle University of Thessaloniki, Department of Informatics (IEEE Fellow), Semantic 3DTV Content Analysis and Description
Professor Dr. Sudip Misra gave a lecture on "Jamming in Wireless Sensor Networks" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/sM0jy
Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
Aggelos Katsaggelos, Professor and AT&T Chair, Northwestern University, Department of Electrical Engineering & Computer Science (IEEE/ SPIE Fellow, IEEE SPS DL), Sparse and Redundant Representations: Theory and Applications
Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/U5nGq
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
1. The document defines intelligence as the ability to reason, understand complex ideas, learn from experience, plan tasks, and solve problems. It also discusses two major definitions of intelligence from scientific reports.
2. Artificial intelligence is defined as giving machines human-like intelligence or the ability to perform tasks normally requiring human intelligence. The document discusses different approaches to AI like systems that think rationally versus like humans.
3. The key approaches discussed are the Turing test to evaluate if a machine can think like a human, cognitive modeling to understand human thinking, and rational agent theory to create agents that act rationally to achieve goals.
- EMIL-A is a cognitive norm-based agent architecture that aims to simulate how norms emerge and are internalized. It includes mental representations like N-beliefs and N-goals that allow norms to guide an agent's behavior.
- The architecture recognizes norms through N-beliefs, adopts norms as N-goals, and uses norm-guided decision making to produce conforming behavior. This allows norms to emerge through agents and become internalized.
- By simulating norm dynamics using EMIL-A, researchers can better understand how and why agents comply with norms, moving beyond just strategic reasoning to include internalization for its own sake
The document discusses the IEEE Signal Processing Society and the Greek signal processing community. It provides a brief history of signal processing and its influences from other fields. It notes the ubiquity of signals and signal processing. It then summarizes the current state and challenges facing the IEEE Signal Processing Society. It provides details on the local Greek SPS chapter, including its size, activities, and plans for coordinating with the broader Greek signal processing community. These plans include making the Greek SP Jam a regular event and establishing workshops, summer schools, lectures, decentralized events, and awards.
Professor Professor Joseph Sifakis gave a lecture on From Programs to Systems – Building a Smarter World in the Distinguished Lecturer Series - Leon The Mathematician.
Ahmed K. Elmagarmid (IEEE Fellow and ACM Distinguished Scientist) gave a lecture on Data Quality: Not Your Typical Database Problem in the Distinguished Lecturer Series - Leon The Mathematician.
Nicholas Kalouptsidis, Professor, National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Nonlinear Communications: Achievable Rates, Estimation, and Decoding
Professor Ivica Crnkovic gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
This document discusses compressive spectral image sensing and optimization. It introduces compressive spectral imaging (CASSI) which uses coded apertures to sense a datacube with only N^2 measurements rather than the traditional N x N x L measurements. Coded apertures can be optimized for sensing and reconstruction performance as well as spectral selectivity and image classification. New families of coded apertures include boolean, spectrally selective, super-resolution, and colored apertures.
This document summarizes a talk on influence propagation in large graphs. It discusses theorems and algorithms related to modeling the spread of information, viruses, and diseases over networks. The document begins by motivating the importance of understanding dynamical processes over networks through examples related to epidemiology, viral marketing, cybersecurity, and more. It then outlines threshold results for epidemic models on static graphs that depend on the largest eigenvalue of the graph's adjacency matrix and properties of the propagation model. The talk discusses proofs of these results and also covers extensions to dynamic graphs and competing viruses. Finally, it discusses algorithms for determining who to immunize to control outbreaks.
Professor Maria Petrou gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Professor Xin Yao gave a lecture on "Co-evolution, games, and social behaviors" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/G7MdD
Georgios Giannakis, Professor and ADC Chair in Wireless Telecommunications, University of Minnesota, Department of Electrical & Computer Engineering (IEEE/EURASIP Fellow, IEEE SPS DL), Sparsity Control for Robustness and Social Data Analysis
Professor Ismail Toroslu gave a lecture on "Web Usage Mining and Using Ontology for Capturing Web Usage Semantic" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Constantine Kotropoulos, Associate Professor, Aristotle University of Thessaloniki, Department of Informatics, Sparse and Low Rank Representations in Music Signal Analysis
This document discusses using model checking techniques for safety critical systems at NASA. It begins by introducing model checking and how it can be used to verify that a program or model satisfies a given property. It then discusses challenges like the state explosion problem and presents compositional verification as a way to address this by breaking the verification task into checking smaller components. The document provides several examples of applying these techniques to real NASA systems like rovers and spacecraft software.
Ioannis Pitas, Professor, Aristotle University of Thessaloniki, Department of Informatics (IEEE Fellow), Semantic 3DTV Content Analysis and Description
Professor Dr. Sudip Misra gave a lecture on "Jamming in Wireless Sensor Networks" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/sM0jy
Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
Aggelos Katsaggelos, Professor and AT&T Chair, Northwestern University, Department of Electrical Engineering & Computer Science (IEEE/ SPIE Fellow, IEEE SPS DL), Sparse and Redundant Representations: Theory and Applications
Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/U5nGq
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
1. The document defines intelligence as the ability to reason, understand complex ideas, learn from experience, plan tasks, and solve problems. It also discusses two major definitions of intelligence from scientific reports.
2. Artificial intelligence is defined as giving machines human-like intelligence or the ability to perform tasks normally requiring human intelligence. The document discusses different approaches to AI like systems that think rationally versus like humans.
3. The key approaches discussed are the Turing test to evaluate if a machine can think like a human, cognitive modeling to understand human thinking, and rational agent theory to create agents that act rationally to achieve goals.
- EMIL-A is a cognitive norm-based agent architecture that aims to simulate how norms emerge and are internalized. It includes mental representations like N-beliefs and N-goals that allow norms to guide an agent's behavior.
- The architecture recognizes norms through N-beliefs, adopts norms as N-goals, and uses norm-guided decision making to produce conforming behavior. This allows norms to emerge through agents and become internalized.
- By simulating norm dynamics using EMIL-A, researchers can better understand how and why agents comply with norms, moving beyond just strategic reasoning to include internalization for its own sake
Thinking involves mental processes such as forming concepts, problem solving, reasoning, and decision making. There are different types of thinking such as autistic thinking and realistic thinking. Cognitive psychology studies mental processes like thinking, perceiving, remembering, and learning. Computer programming draws on skills also used in writing like creativity, logic, and sequencing, and can benefit from understanding cognitive psychology which studies how people think. Problem solving is considered one of the most complex intellectual functions and involves identifying problems, exploring solutions, choosing an action, and evaluating outcomes. Reasoning allows transforming information to reach conclusions through deductive or inductive logic.
This chapter discusses theory building. Theories are conceptual frameworks used to explain and predict phenomena. Building theory involves developing increasingly abstract concepts and propositions that describe relationships between variables. A hypothesis connects concepts at the abstract level to empirically testable statements at the observable level. The process of theory building involves assessing existing knowledge, formulating concepts and propositions, stating hypotheses, acquiring empirical data to test hypotheses, and using the results to provide explanations or identify new problems.
Intelligence refers to mental abilities such as reasoning, problem-solving, and learning. While efforts have been made to define and measure intelligence quantitatively, it remains a complex construct. Theories of intelligence include Spearman's two-factor theory, Cattell's fluid and crystallized intelligence, and Gardner's theory of multiple intelligences. Intelligence testing has practical and scientific significance but also controversies regarding definitions, biases, and appropriate uses. The most influential tests are Wechsler's which view intelligence as related verbal and non-verbal abilities. Continued revisions aim to make tests concurrent while new norms update comparisons.
The document discusses several key aspects of the Cognitive School of strategy formation:
1. Cognition refers to processes like thinking, learning, judging, problem solving, and memory. The Cognitive School views strategy formation as a cognitive process that occurs in the mind of the strategist.
2. Strategists perceive and interpret the objective environment through "distorting filters" like concepts, maps, and schemas formed by their own cognition. This leads to different perceived environments across strategists and organizations.
3. The Cognitive School premises that strategies emerge from a strategist's perspectives and are difficult to obtain, optimize, and change due to the subjective nature of human cognition. Strategies depend on individual cognitive capabilities.
The document discusses various theories of intelligence including general intelligence, multiple intelligences, emotional intelligence, and practical and creative intelligence. It also examines the genetic and environmental influences on intelligence, differences in intelligence scores between demographic groups, and controversies around measuring and quantifying intelligence.
This document discusses copyright and permission related to a publication by Leaders Excellence, Inc. It states that no part of the publication may be reproduced without prior written permission, except for brief quotations for non-commercial uses permitted by copyright law. It provides the publisher's contact information for requests for permission. The document then provides a table of contents for the publication which discusses various aspects of critical thinking across 10 chapters.
Decision-making is usually a secondary topic in psychology, relegated to the last chapters of textbooks. Most of the time these chapters acknowledge the failure of the “homo economicus” model and propose to understand human irrationality as the product of heuristic and biases, which may be rational under certain environmental conditions. Psychology pictures decision-making as a deliberative task, studied by multiple-choice tests using the traditional paper and pen method. Psychological research on decision-making assumes that the subjects’ competence in probabilistic reasoning – as revealed by these tests – is a good description of their decision-making capacities. This conception takes for granted (1) that the process of reasoning about action is identical to the process of decision-making and (2) that psychology documents either human failures to comply with rational-choice standards or how mental mechanisms are ecologically rational. In this talk, I argue that decision neuroscience (“neuroeconomics”) may suggest another approach for the study and the nature of decision-making. Research in this field show that information processing in decision is affective, embodied and prosocial: Evolutionary older neural structures, such as the limbic system or dopaminergic neurons, are highly involved in subjective risk and certainty assessment; somatosensory information is integrated in prefrontal areas and helps evaluating choices; In games where players may adopt fair or unfair attitudes, the first ones tend to be more frequent and the second ones elicit emotionally negative reaction.
Moreover, I suggest (against bounded rationality) that these mechanisms achieve near-optimality in social decision-making and (against ecological rationality) that this optimality is not fitness-enhancing. Consequently, I argue that the study of decision-making should be construed as an investigation into “natural rationality” (the mechanisms by which cognitive agents make decisions) and that decision-making should be a central concern for psychology.
A book review on the book of John Adair,titled Effective decision making presented by Dr. Helal Uddin Ahmed, Bangladeshi doctor works in psychiatry, BSMMU, Bangladesh.
Mental state is one important topic of artificial general intelligence (AGI). In this talk. we’ll investigate how to understand the mental states pf AI systems, have an introduction to reservoir computing, the appropriate computing model, and share some examples and open source projects.
心靈狀態 (mental state) 是 強人工智慧 (AGI) 研究的一個重要問題。本演講將探討,在哲學或數學上該如何看待人工智慧系統的心靈狀態,介紹水庫運算 (reservoir computing) 此一適合的運算模型,並展示水庫運算的實例與開源專案。
1) The document discusses using implicit tests to measure consumer perceptions beyond what they explicitly say. It explains how implicit tests can reveal unconsciously associated values with brands or products.
2) A case study is described where a brand wants to develop new packaging that conveys key values. Implicit association tests were used along with explicit questioning to evaluate how well 5 prototype packs conveyed the brand's values.
3) The results showed the top performing prototype packs increased positive purchase intent compared to the current packaging, though the current pack still had the highest intent. Implicit tests provided insights beyond what consumers explicitly reported.
Scenario planning is a strategic planning method used to prepare for potential future events and situations. It involves identifying key factors that could significantly impact the future, developing scenarios around different combinations of those factors, and determining how to respond under each scenario. The key steps are to identify sources of uncertainty, build scenarios by exploring how the uncertainties might interact, estimate the likelihood of each scenario, identify early signs or "trigger points", develop action plans, and monitor for trigger points during implementation. Scenario planning helps organizations make better decisions and be more resilient when facing an unpredictable future.
The document discusses various aspects of thinking, language, and communication. It covers topics such as defining thinking, the thinking process, concepts, problem solving, decision making, creative thinking, and language elements. Some key points include:
- Thinking involves the cognitive manipulation of information from the environment and symbols stored in long-term memory. Language and images are types of symbols used in thinking.
- Concepts are symbolic constructions that represent common features of objects or events and allow us to classify things. Concepts can be acquired naturally, through discrimination learning, or by definition.
- Problem solving involves reducing the discrepancy between the current situation and the desired goal state. It uses algorithms, heuristics, means-end
A short journey across several technical subjects to help people to be open-minded.
Video (Spanish): https://youtu.be/CILmSB90swY
Podcast (Spanish): https://t.co/WGthGnyWKt
This document discusses the concepts and processes involved in theory building for business research. It defines key terms like concepts, propositions, hypotheses, variables, and levels of abstraction. Concepts are abstract ideas that represent classes of objects or occurrences, and are building blocks for theories. Propositions are statements about relationships between concepts. Hypotheses, which exist at the empirical level, are testable statements about relationships between variables. The scientific method involves using both deductive and inductive reasoning to develop theories through increasingly abstract conceptualization, then empirically validating those concepts.
This document provides an overview of artificial intelligence techniques. It begins with definitions of AI and discusses branches of AI like logical AI, search, pattern recognition, knowledge representation, inference and more. It also discusses AI applications, problems in AI and the levels of modeling human intelligence. Several examples are then provided to illustrate increasingly sophisticated AI techniques for playing tic-tac-toe and answering questions to demonstrate moving towards knowledge representations that generalize information and are more extensible.
PGCAP, cohort 2: core module week 1: reflecting and developingAcademic Development
This document introduces reflection and peer observation as part of the UK Professional Standards Framework. It discusses the importance of reflection for continuing professional development in higher education. Key points covered include defining reflection, models for reflective practice, the benefits of sharing reflections with peers, and using different media to record reflections. The document also introduces the UK Professional Standards Framework and its six areas of activity and core knowledge.
The document defines different approaches to artificial intelligence including:
1. Systems that think like humans through cognitive modeling of human thought processes.
2. Systems that think rationally by following logical rules and principles like Aristotle's laws of thought.
3. Systems that act rationally by perceiving the environment, acting to achieve goals based on beliefs, and being modeled as rational agents.
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Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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4. An Agent on the London Underground
Goal: if there is an emergency
then I deal with it myself
or I get help or I escape.
Beliefs: I get help if there is an emergency
and I am on a train
and I alert the driver of the train.
there is an emergency
if there is a fire. I alert the driver of the train
if I press the alarm button.
Observe Decide
Act
The World
4
5. Complex thinking and decision‐making can be compiled into
more efficient heuristics
For example: if there is a fire
and I am on a train
and I can not deal with the fire myself
then I press the alarm button.
Lower‐level heuristics and higher‐level thinking and deciding can
be combined, as in dual process models of human thinking.
As Kahneman and Frederick (2002) put it:
the intuitive, subconscious level “quickly proposes intuitive
answers to judgement problems as they arise”,
while the deliberative, conscious level “monitors the quality of
these proposals, which it may endorse, correct, or override”.
5
10. Simplified Abductive Logic Programming (ALP) agent cycle
Maintenance goals
Achievement goals
Beliefs Beliefs
Candidate Consequences Decide:
plans of Beliefs Judge
action probabilities
and utilities
short cuts that achieve
Observations higher‐level goals implicitly
Actions
The World
10
11. The syntax of goals and beliefs in ALP agents
Beliefs are logic programs:
conclusion if condition1 …. and conditionn
equivalently: if condition1 …. and conditionn then conclusion
Goals are clauses in first‐order logic (FOL):
If condition1 …. and conditionn
then conclusion1 …. or conclusionm
All variables are universally quantified.
11
12. ALP agents – minimal model semantics
Beliefs B describe the world as the agent imagines it.
Goals G describe the world as the agent would like it to be.
Given observations O,
the agent’s task is to generate a set
of actions and assumptions such that:
G O is true in the minimal model determined by B .
12
15. Goal G: if there is an emergency
then I deal with it myself
or I get help or I escape
Observation O: there is smoke
Beliefs B: there is smoke if there is a fire
there is an emergency if there is a fire
I get help if I press the alarm button
G O is true in the minimal model determined by B , where
= {there is a fire, I press the alarm button}.
explains there is smoke achieves I get help
(abduction) (planning)
15
18. The ALP agent model can help agents
make better decisions.
In classical decision theory, all alternative actions and their
consequences (outcomes) are assumed given in advance.
In ALP agents, alternative actions and their consequences are
generated by using beliefs to search for solutions of goals.
The same evaluation criteria can be used both
to decide between alternatives and
to guide the search.
18
19. A theoretical framework for goal‐based choice and for prescriptive analysis.
Kurt A. Carlson & Chris Janiszewski & Ralph L. Keeney & David H. Krantz &
Howard C. Kunreuther & Mary Frances Luce & J. Edward Russo & Stijn M. J.
van Osselaer & Detlof von Winterfeldt. Market Lett (2008) 19:241–254.
“We view consumer preferences and consumer decisions as the output
of goal pursuit. This departure from rational economic models allows us
to characterize consumer behavior more fully.
For example, instead of assuming that consumer choices are the simple
output of application of one’s utility function to a set of known
alternatives, with known consequences, we assume that choices result
from consumers’ attempt to satisfy numerous goals of different types
(e.g., process, outcome, social), under a variety of constraints.“
21. ALP as the Language of Thought (LOT)
In the philosophy of language, there are three schools of thought :
The LOT is a private, language‐like representation,
which is independent of public, natural languages.
The LOT is a form of public, natural language;
and the natural languages that we speak influence the way we think.
Human thinking does not have a language‐like structure at all.
In ALP agents, clausal logic serves as an agent’s private LOT,
independent of any public language.
21
23. The Emergency Notice on the London underground
Press the alarm signal button to alert the driver.
The driver will stop
if any part of the train is in a station.
If not, the train will continue to the next station,
where help can more easily be given.
There is a 50 pound penalty for improper use.
23
24. The Meaning of the London Underground Notice
the driver is alerted
if you press the alarm signal button.
the driver will stop the train in a station
if the driver is alerted
and any part of the train is in the station.
the driver will stop the train in the next station
if the driver is alerted
and not any part of the train is in a station.
help can more easily be given in an emergency
if the train is in a station.
You may be liable to a £50 penalty
if you use the alarm signal button improperly
24
25.
26. 1.-(1) A person born in the United Kingdom after
commencement shall be a British citizen if at the time of the
birth his father or mother is –
(a) a British citizen; or
(b) settled in the United Kingdom.
The logic of subsection 1.‐(1)
A person shall be a British citizen by 1.‐(1)
if the person was born in the United Kingdom
and the person was born after commencement
and a parent of the person was a British citizen
at the time of the person’s birth or
a parent of the person was settled in the United
Kingdom at the time of the person’s birth.
27. The problems of understanding
natural language communications
1. Identify the intended meaning of ambiguous sentences. e.g.
clarity
he gave her the book.
2. Represent the intended meaning, e.g.
John gave Mary the book.
John gave the book to Mary.
Mary received the book from John. simplicity
The book was given to Mary by John.
in a simple canonical form. e.g.
give(john, book, mary, e1000)
3. Connect the canonical representation
with other mental representations, coherence
in a way that makes it easy to use the representation later.
27
28. Clausal logic is a canonical form of FOL.
In clausal logic, sentences have a simplified form, e.g.:
has‐feathers(X) ← bird(X).
bird(john).
In standard FOL, the same beliefs can be expressed in infinitely many,
equivalent ways, including:
¬(X((¬has‐feathers (X) bird(X)) ¬bird(john)))
¬(X((¬has‐feathers (X) ¬bird(john)) (bird(X) ¬bird(john))))
In clausal logic, reasoning is simpler than in standard FOL
and can be reduced to forward or backward reasoning,
which are special cases of the resolution rule.
28
31. Williams: Two Principles of Coherence
“1. Put at the beginning of a sentence those ideas that you have
already mentioned, referred to, or implied, or concepts that you
can reasonable assume your reader is already familiar with, and
will readily recognise.”
2. Put at the end of your sentence the newest, the most
surprising, the most significant information: information that you
want to stress – perhaps the information that you will expand on
in your next sentence.”
32. Coherence
Example: A.
If A then B.
If B then C.
Therefore C.
Example: C?
C if B.
B if A.
A.
Therefore C.
34. A Connectionist implementation of ALP
Goal: if there is an emergency
then I deal with it myself
or I get help or I escape.
Beliefs: I get help if there is an emergency
and I am on a train
and I alert the driver of the train.
there is an emergency
if there is a fire. I alert the driver of the train
if I press the alarm button.
36. Conclusions
• The ALP agent model can unify
o Logic
o Production Systems
o Probability
o Decision Theory
o Connectionism
• The ALP agent model can be used to
improve human decision making.
• ALP clausal logic can be used to
improve human communcation.
36
39. Connection graphs can combine
logic, search, connectionism,
learning and decision making
• Links can be weighted by statistics about how often they
have contributed to successful outcomes in the past (and
how likely they are to contribute in the future).
• Input observations and goals can be assigned different
strengths (or utilities).
• The strength of observations and goals can be propagated
through the graph in proportion to the weights on the links.
• Activating links with the currently highest weighted
strengths implements a form of best‐first search
for a solution with highest expected utility, and
is similar to the activation networks of Patie Maes.
41. A theoretical framework for goal‐based choice and for prescriptive analysis.
Kurt A. Carlson & Chris Janiszewski & Ralph L. Keeney & David H. Krantz &
Howard C. Kunreuther & Mary Frances Luce & J. Edward Russo & Stijn M. J.
van Osselaer & Detlof von Winterfeldt. Market Lett (2008) 19:241–254.
“We view consumer preferences and consumer decisions as the output
of goal pursuit. This departure from rational economic models allows us
to characterize consumer behavior more fully.
For example, instead of assuming that consumer choices are the simple
output of application of one’s utility function to a set of known
alternatives, with known consequences, we assume that choices result
from consumers’ attempt to satisfy numerous goals of different types
(e.g., process, outcome, social), under a variety of constraints.“
43. (2) A new-born infant who, after commencement, is found abandoned
in the United Kingdom shall, unless the contrary is shown, be deemed
for the purposes of subsection (1) –
(a) to have been born in the United Kingdom
after commencement; and
(b) to have been born to a parent who at the time of the
birth was a British citizen or settled in the United Kingdom.
The logic of subsection 1.‐(2) combines object language and meta‐language:
It shall be assumed that a person satisfies the conditions of subsection (1)
if the person is a new‐born infant found
abandoned in the United Kingdom after commencement
and it is not shown
that the person does not satisfy the conditions of subsection (1)