Center for Analysis and Design of Intelligent Agents public:t-713-mers:mers-25
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2026-04-16T21:46:23+00:00Center for Analysis and Design of Intelligent Agents
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DCS-T-713-MERS-2025 Main
Lecture Notes
Empirical Reasoning IV: AI Architectures
System Architecture
What it is In CS: the organization of the software that implements a system.
In AI: The total system that has direct and independent control of the behavior of an Agent via its sensors and effectors.text/html2025-08-09T12:02:26+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:anns
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A SHORT CRASH COURSE IN ANNs
ANNs: Overview
ANNs A special way to create classification functions over large amounts of data without explicitly specifying the mathematical operations. Instead, a largely automated process called 'training' (based on an algorithm calledtext/html2025-10-21T08:13:21+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:causation-methodology-architecture
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DCS-T-713-MERS-2025 Main
Lecture Notes
Empirical Reasoning (III)
Worlds & Regularity
Noise A world with no regularity is a completely unpredictable world.
In such worlds, learning is impossible. Complete Regularity Worlds with complete regularity are deterministictext/html2025-08-19T12:27:47+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:concepts_terms
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T-713-MERS-2025 Main
Link to Lecture Notes
INTRODUCTION
Working Definition of Intelligence
The (working)
Definition of
Intelligence
Used in This
Course
Adaptation with insufficient knowledge and resources
-- Pei Wang
'Adaptation' means changing strategically in light of new information.text/html2025-08-26T09:39:48+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:empirical-reasoning-1
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DCS-T-713-MERS-2025 Main
Lecture Notes
Empirical Reasoning (I)
Foundational Considerations
Why Empirical? The concept 'empirical' refers to the physical world: We (humans) live in a physical world, which is to some extent governed by rules, some of which we know something about.text/html2025-08-26T09:40:22+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:empirical-reasoning-2
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DCS-T-713-MERS-2025 Main
Lecture Notes
Empirical Reasoning (II)
Uncertainty in Physical Worlds
What it is In a dynamic world with a large number of elements and processes, presenting infinite combinatorics, knowing everything is impossible and thus predicting everything is also impossible.text/html2025-08-18T18:10:00+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:empirical_reasoning
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T-713-MERS-2025 Main
Link to Lecture Notes
EMPIRICAL REASONING - Prelude
Reasoning
What is Reasoning? A systematic way of thinking about and manipulating models of relations.
A systematic way to create, compare, manipulate and apply models of relations. How is it done?text/html2025-08-19T15:57:45+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:empiricism
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T-713-MERS-2025 Main
Link to Lecture Notes
WORLDS, EMPIRICISM & DATA
The Physical World
What it is A set of ultimate constraints that determine what is and isn't possible. We call it “the laws of physics” (even though we don't really know if they are immutable 'laws').text/html2025-08-26T09:35:31+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:knowledge
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DCS-T-713-MERS-2025 Main
Lecture Notes
Learning & Knowledge
Key Learning Terms
What it is Learning is a process that has the purpose of generating actionable information, a.k.a. knowledge.
Key Features Inherits key features of any process:
- Purpose: To adapt, to respond in rational ways to problems / to achieve foreseen goals; this factor determines how the rest of the features in this list are measured.text/html2025-08-26T09:42:16+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:last_questions
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DCS-T-713-MERS-2025 Main
Lecture Notes
Last Questions
"State-of-the-Art" Reasoning
What is Reasoning Production of plans, given rules, environment, and goals. Reasoning is Involved with Learning Taking reasoning out of runtime operations means that plans must be created a-priori.text/html2025-09-25T08:05:01+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:learning_outcomes
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After taking the course, diligently attending the classes and doing the assignments, thoroughly reading, and actively participating in discussions, students should be able to:
* Describe how common forms of reasoning relate to next-generation AI systemstext/html2025-08-26T10:46:58+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:lecture_notes
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T-713-MERS-2025 Links to Thórisson's Lecture Notes
* Background Concepts
* Artificial Neural Networks (ANNs)
* W1: INTRODUCTION
* Concepts & Terms
* Empirical Reasoning - Preface
* W2: DATA, WORLDS & EMPIRICISM
* Key concepts: Environment, tasks, entities, variables, experiments, data
* Worlds, Empiricism
* W3:text/html2025-11-17T13:59:36+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:main
http://cadia.ru.is/wiki/public:t-713-mers:mers-25:main?rev=1763387976&do=diff
T-720-MERS, EMPIRICAL REASONING AI SYSTEMS, Fall 2025
OVERVIEW
See Canvas For Up-To-Date Information, Notifications, Links to Lecture Notes
Instructor: Kristinn R. Thórisson
Teaching Assistants: Leonard Eberding, Chloe Schaff
8 ECTS Units, full Master's-level coursetext/html2025-08-26T09:41:53+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:methodology
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DCS-T-713-MERS-2025 Main
Lecture Notes
Methodological Considerations
Methodology
What it is The methods - tools and techniques - we use to study a phenomenon.
The scientific methodology of any field is derived from the prevailing scientific theory/ies in that field.text/html2025-10-08T11:23:35+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:nal-assignment-1
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DCS-T-713-MERS-2025 Main
Engineering Projects
NARS Assignment 2: Knowledge Transfer in OpenNARS for Applications (ONA)
The aim of this assignment is to introduce you to a general-purpose AI system, OpenNARS-for-Applications (ONA), and to familiarize you with how ONA transfers and generalizes knowledge.text/html2025-09-25T08:12:00+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:organization
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DCS-T-713-MERS-2025 Main
Organization
Your final grade will be made up of in-class assignments, home assignments, and the final exam. The exact percentages will be adjusted towards the end of the course. This is intended to provide you with an overview and general idea.text/html2025-10-02T10:45:43+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:readings
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DCS-T-713-MERS-2025 Main
T-713-MERS-2025 Readings & Study Material
Readings README (Do not skip!)
INTELLIGENCE: THE PHENOMENON [5,6]
What is intelligence?
How do experts talk about it?
What has been uncovered?
What uniquely separates the phenomenon of intelligence from other similar phenomena in the world?text/html2025-09-25T11:06:09+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:reasoning-intro
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DCS-T-713-MERS-2025 Main
Lecture Notes
Introduction to Reasoning Machines
Syllogisms
What is it? A form of deductive argument/reasoning in which a conclusion is drawn from 2 given or assumed propositions (premises / statements). The premises and the conclusion are simple declarative statements constructed using only three simple terms between them, each term appearing twice (as a subject and as a predicate)text/html2025-09-02T08:17:01+00:00Anonymous (anonymous@undisclosed.example.com)public:t-713-mers:mers-25:task_theory
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T-713-MERS-2025 Main
Link to Lecture Notes
Task Teory
What it is A systematic framework for describing, comparing, and analyzing tasks, independent of any specific agent. Provides the foundations for evaluating intelligent systems empirically (measurable outcomes, repeatable experiments, controlled variables).