INFR08010 Informatics 2d Reasoning And Agents Notes | Assignment Help | Syllabus
Country: United Kingdom
University: The University Of Edinburgh
This course focuses on methods for addressing real-world inference problems that include representation, reasoning, and planning. The course emphasizes the significance of I using a smart representation of information that facilitates efficient reasoning, and (ii) utilizing task constraints for intelligent search and planning. The concept of expressing action, location, and time is formalized in the context of agents capable of perceiving their surroundings and acting on them. The capacity to cope with uncertain data in real-world situations is also emphasized, thus planning and reasoning techniques are expanded to incorporate inference in probabilistic domains.
1. Introduction to Intelligent Agents * Agents’ characteristics, performance metrics, and surroundings
* An example thread (Programming environment setup) for the Wumpus World Problem
2. Planning Based on a Search
* Searching as Planning Problem: In settings that are predictable, observable, static, and well-known.
* Using Constraints in Smart Searching 1
* Memoisation/Smart Searching 2: Exploiting Subproblems
* Agents’ Informed Search and Exploration
3. Planning and Logical Representation
* A Reconsideration of Propositional Logic (Shortcomings)
* First-Order Logic and Fact/Rule Encoding in FOL
* Propositional and FOL Calculus Inference Rules
* Generalized Modus Ponens and Unification
* Using resolution-based inference to guide search
* Wumpus Example of Knowledge Representation: Using FOL to Represent Action, Space, and Time
* Calculus of Situations: Representing time in plans
4. Planning for Complex Tasks on a Large Scale
* STRIPS: Representing States, Goals, and Actions
* Planning for Partial Orders
* Taking Action and Planning in the Real World
5. Taking Action in Uncertain (Real-World) Situations
* Bayesian Network Representation
* Bayes Net Probabilistic Reasoning
* Wumpus World Revisited: Planning amid Uncertainty
* Hidden Markov Models in Probabilistic Reasoning over Time I
* Dynamic Bayesian networks in Probabilistic Reasoning over Time II
* Markov Decision Processes are a kind of decision-making algorithm.
Artificial Intelligence, Human-Computer Interaction (HCI), Intelligent Information Systems Technologies, Simulation and Modelling are all relevant QAA Computing Curriculum Sections.
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