Artificial Intelligence 2004 Planning: Situation Calculus
|
|
- Elisabeth Douglas
- 5 years ago
- Views:
Transcription
1 Artificial Intelligence 2004 Planning: Situation Calculus Review STRIPS POP Hierarchical Planning Situation Calculus (John McCarthy) situations actions axioms Review Planning 1 STRIPS (Nils J. Nilsson) actions specified by preconditions and effects stated as formulae in (restricted) First-Order Predicate Logic planning as search in space of (world) states plan is sequence of actions from start to goal state Partial Order Planning planning through plan refinement parallel expansion to satisfy preconditions causal links (effect of a used in precondition of a') threats (effect of a negates precondition of a'; a'<a)
2 Review Planning 2 Plan Decomposition / Hierarchical Planning hierarchical organisation of 'actions' complex and less complex (or: abstract) actions lowest level reflects directly executable actions planning starts with complex action on top plan constructed through action decomposition substitute complex action with plan of less complex actions (pre-defined plan schemata; or learning of plans/plan abstraction, see ABSTRIPS) overall plan must generate effect of complex action Situation Calculus - Overview Situation Calculus (John McCarthy) models actions and events in First-Order Predicate Logic situation as additional parameter for some formulae (fluents) allows to specify change due to events action applied in situation: Result-function effect (changes) and frame (remain) of an action specified through axioms planning as theorem-proving
3 Situations A situation corresponds to a World State. Situations are denoted through constants and variables s, s' (reification) Situations are described through FOPL formulae. Actions transform situations. Situations - Blocks World Example Situation s 0 s 0 = {on(a,b),on(b,fl),clear(a),clear(fl)} on(a,b,s 0 ),on(b,fl,s 0 ),clear(a,s 0 ), clear(fl,s 0 ) Action: move (A, B, Fl) Situation s 1 A A B B s 1 = {on(a,fl), on(b,fl), clear(a),clear(b),clear(fl)} on(a,f,s 1 ),on(b,fl,s 1 ),clear(a,s 1 ),clear(b,s 1 ),clear(fl,s 1 )
4 Actions Actions are written like functions with their name and parameter list. They can also be referred to by variables (reification). Actions transform situations. The performance of an action in a situation is denoted through the result (do) function. The performance of an action a in a situation s yields a new situation s'. Result Function Result (or: do) is a function from actions and situations into situations. Example s' = Result (move (x, y, z), s) specifies a new situation s' which is the result of performing a move-action in situation s. General s = Result (a, s) for action a and situations s, s
5 Result Function - Example situation s = {on(a,b), on(b,fl), clear(c)} action a = move (A,B,C) apply action a in situation s Result (move (A,B,C), s) = s' s' = {on(a,c), on(b,fl), clear (B)} Fluents Formulas affected by actions have situations as parameters. Predicates and functions in these formulae which change due to actions are called fluents. Integrate situation parameter into these fluent formulas. on(a,b,s), on(b,fl,s), clear(c,s) Note: Block(A), Block(B),... without s
6 Situations in Formulas situation s action a on(a,b,s), on(b,fl,s), clear(c,s) move (A,B,C) apply action a in situation s Result (a,s) Result (move (A,B,C), s) = s' situation s' on(a,c,s'), on(b,fl,s'), clear(b,s') Now the Calculus
7 Situation Calculus Effect axioms describe how an action changes a situation when the action is performed. Frame axioms describe what remains unchanged between situations. Successor-state axioms combine effect and frame axioms for an action. action move (x, y, z) effect-axiom: Effect Axiom on (x, y, s) clear (z, s) x z on (x, z, Result (move (x, y, z), s)) Explanation: If the left side (condition) of the axiom holds, then the action can be performed, and the right side (consequence) follows. The consequence states what is true in the resulting situation, here: on(x,z)
8 action move (x, y, z) Frame Axiom Frame Axiom - example : on (x, y, s) x u on (x, y, Result (move (u, v, z), s)) Explanation: A Frame Axiom states what remains true or unaffected when an action is performed. In the example here: the blocks x, y which are not moved remain where they are, on (x, y) Action Description - Axioms Axioms specify what changes and what remains. Consider every combination of action and fluent. effect-axioms specify effects, what changes positive effects state what becomes true negative effects state what becomes false frame-axioms specify frame, what remains positive effects state what remains true negative effects state what remains false In addition, general axioms specify general laws or rules of the domain.
9 Action move (x, y, z) Effect Axioms effect-axioms specify change (for pair move-on) positive effect on (x, y, s) clear (x, s) clear (z, s) y z on (x, z, Result (move (x, y, z), s)) If x is on y and both x and z are clear, then the moveaction can be performed and the result is that x is on z. negative effect on (x, y, s) clear (x, s) clear (z, s) y z on (x, y, Result (move (x, y, z), s)) If x is on y and both x and z are clear, then the moveaction can be performed and the result is that x is not anymore on y. Frame Axioms Action move (x, y, z) frame-axioms specify frame, i.e. what remains positive frame on (x, y, s) x u on (x, y, Result (move (u, v, z), s)) If a block x is on another block y, and x is not moved, then it stays on y. negative frame on (x, y, s) (x u y z) on (x, y, Result (move (u, v, z), s)) If a block x is not on another block y, and x is not moved, nor is something put on y, then x will still not be on y after the move.
10 Situation Calculus Successor-State Axioms successor-state-axioms combine frame and effect axioms; specified for each fluent general structure predicate expression true in follow state the action made it true; it was true and the action did not make it false. Successor-State Axiom - Example Successor-state axiom for on(x,y,s) x,y,s,a: on (x, y, Result(a,s)) [a = move(x,z,y) clear(x,s) clear(z,s) y z ] [on(x,y,s) a move(x,z,y) ] First part specifies action achieving on(x,y) Second part specifies actions not deleting on(x,y)
11 General axioms Situation Calculus - General Axioms Formulas which are true in all situations or states. Example: x, y, s: on (x, y, s) (y=table) clear (y, s) For all situations s and all objects x and y: if something is on object y in s, and y is not the table, then y is not clear in s. s: clear (Table, s) The table (or floor) is always clear. Situation Calculus - Problems Frame-Problem specify everything which remains stable Leads to too many conditions which would have to be explicitly stated for any state transformation. Computationally very expensive. Approach: successor-state axioms; STRIPS Qualification-Problem specify everything which is precondition to an action Difficult to include every precondition which could prevent (if not fulfilled) the action to be performed). Approach: non-monotonic reasoning with standard preconditions and effects as defaults.
12 Situation Calculus - Problems Ramification-Problem derived formulae conflict between change and frame Some axioms state conclusions about fluents indirectly affected by actions. This can contradict frame-axioms. Example: An agent grabs an object and holds it. When the agent moves, the object moves too, though not explicitly stated. Rule 1: every object stays where it is unless it is moved. Rule 2: if an object is attached to another object and one of the objects moves, the other object moves too. Approach: Integrate TMS for derived formulae. Situation Calculus and Planning Planning starts with a specified start situation and the specification of a goal situation. Planning comprises of finding a proof which infers the goal situation from the start situation using successor-state and other axioms. A Plan is a sequence of actions which specifies a sequence of transformations of situations from the initial situation to the final situation.
13 Additional References Nils J. Nilsson: Artificial Intelligence A New Synthesis. Morgan Kaufmann, San Francisco, 1998.
Artificial Intelligence 2005/06
Planning: STRIPS 74.419 rtificial Intelligence 2005/06 Planning: STRIPS STRIPS (Nils J. Nilsson) actions are specified by preconditions and effects stated as restricted FOPL formulae planning is search
More informationSet 9: Planning Classical Planning Systems. ICS 271 Fall 2013
Set 9: Planning Classical Planning Systems ICS 271 Fall 2013 Outline: Planning Classical Planning: Situation calculus PDDL: Planning domain definition language STRIPS Planning Planning graphs Readings:
More informationSet 9: Planning Classical Planning Systems. ICS 271 Fall 2014
Set 9: Planning Classical Planning Systems ICS 271 Fall 2014 Planning environments Classical Planning: Outline: Planning Situation calculus PDDL: Planning domain definition language STRIPS Planning Planning
More informationPrimitive goal based ideas
Primitive goal based ideas Once you have the gold, your goal is to get back home s Holding( Gold, s) GoalLocation([1,1], s) How to work out actions to achieve the goal? Inference: Lots more axioms. Explodes.
More informationPlanning: STRIPS and POP planners
S 57 Introduction to I Lecture 8 Planning: STRIPS and POP planners Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Representation of actions, situations, events Propositional and first order logic
More information1 What is Planning? automatic programming. cis716-spring2004-parsons-lect17 2
PLANNING 1 What is Planning? Key problem facing agent is deciding what to do. We want agents to be taskable: give them goals to achieve, have them decide for themselves how to achieve them. Basic idea
More informationCS 2750 Foundations of AI Lecture 17. Planning. Planning
S 2750 Foundations of I Lecture 17 Planning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Planning Planning problem: find a sequence of actions that achieves some goal an instance of a search
More informationCS 621 Artificial Intelligence. Lecture 31-25/10/05. Prof. Pushpak Bhattacharyya. Planning
CS 621 Artificial Intelligence Lecture 31-25/10/05 Prof. Pushpak Bhattacharyya Planning 1 Planning Definition : Planning is arranging a sequence of actions to achieve a goal. Uses core areas of AI like
More informationA deterministic action is a partial function from states to states. It is partial because not every action can be carried out in every state
CmSc310 Artificial Intelligence Classical Planning 1. Introduction Planning is about how an agent achieves its goals. To achieve anything but the simplest goals, an agent must reason about its future.
More informationPlanning II. Introduction to Artificial Intelligence CSE 150 Lecture 12 May 15, 2007
Planning II Introduction to Artificial Intelligence CSE 150 Lecture 12 May 15, 2007 Administration Your second to last Programming Assignment is up - start NOW, you don t have a lot of time The PRIZE:
More information3. Knowledge Representation, Reasoning, and Planning
3. Knowledge Representation, Reasoning, and Planning 3.1 Common Sense Knowledge 3.2 Knowledge Representation Networks 3.3 Reasoning Propositional Logic Predicate Logic: PROLOG 3.4 Planning Planning vs.
More informationPlanning. Introduction
Introduction vs. Problem-Solving Representation in Systems Situation Calculus The Frame Problem STRIPS representation language Blocks World with State-Space Search Progression Algorithms Regression Algorithms
More informationArtificial Intelligence. Planning
Artificial Intelligence Planning Planning Planning agent Similar to previous problem solving agents Constructs plans that achieve its goals, then executes them Differs in way it represents and searches
More informationPlanning. Outside Materials (see Materials page)
Planning Outside Materials (see Materials page) What is Planning? Given: A way to describe the world An ini
More information3. Knowledge Representation, Reasoning, and Planning
3. Knowledge Representation, Reasoning, and Planning 3.1 Common Sense Knowledge 3.2 Knowledge Representation Networks 3.3 Reasoning Propositional Logic Predicate Logic: PROLOG 3.4 Planning Introduction
More informationCPS 270: Artificial Intelligence Planning
CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Planning Instructor: Vincent Conitzer Planning We studied how to take actions in the world (search) We studied how to represent
More informationArtificial Intelligence
Artificial Intelligence CSC348 Unit 4: Reasoning, change and planning Syedur Rahman Lecturer, CSE Department North South University syedur.rahman@wolfson.oxon.org Artificial Intelligence: Lecture Notes
More informationArtificial Intelligence Planning
Artificial Intelligence Planning Instructor: Vincent Conitzer Planning We studied how to take actions in the world (search) We studied how to represent objects, relations, etc. (logic) Now we will combine
More informationPlanning. Some material taken from D. Lin, J-C Latombe
RN, Chapter 11 Planning Some material taken from D. Lin, J-C Latombe 1 Logical Agents Reasoning [Ch 6] Propositional Logic [Ch 7] Predicate Calculus Representation [Ch 8] Inference [Ch 9] Implemented Systems
More informationWhere are we? Informatics 2D Reasoning and Agents Semester 2, Planning with state-space search. Planning with state-space search
Informatics 2D Reasoning and Agents Semester 2, 2018 2019 Alex Lascarides alex@inf.ed.ac.uk Where are we? Last time... we defined the planning problem discussed problem with using search and logic in planning
More informationMilestone State Formulation Methods
Milestone State Formulation Methods Hyungoo Han Department of Computer Science & Engineering Hankuk University of Foreign Studies 89 Wangsan-ri Mohyeon Cheoin-gu Yongin-si, Gyeonggi-do 449-791 South Korea
More informationPlanning Algorithms Properties Soundness
Chapter MK:VI III. Planning Agent Systems Deductive Reasoning Agents Planning Language Planning Algorithms State-Space Planning Plan-Space Planning HTN Planning Complexity of Planning Problems Extensions
More informationPlanning and Acting. CITS3001 Algorithms, Agents and Artificial Intelligence. 2018, Semester 2
Planning and Acting CITS3001 Algorithms, Agents and Artificial Intelligence Tim French School of Computer Science and Software Engineering The University of Western Australia 2018, Semester 2 Summary We
More informationPlanning. Introduction
Planning Introduction Planning vs. Problem-Solving Representation in Planning Systems Situation Calculus The Frame Problem STRIPS representation language Blocks World Planning with State-Space Search Progression
More informationPlanning (What to do next?) (What to do next?)
Planning (What to do next?) (What to do next?) (What to do next?) (What to do next?) (What to do next?) (What to do next?) CSC3203 - AI in Games 2 Level 12: Planning YOUR MISSION learn about how to create
More informationClassical Planning. CS 486/686: Introduction to Artificial Intelligence Winter 2016
Classical Planning CS 486/686: Introduction to Artificial Intelligence Winter 2016 1 Classical Planning A plan is a collection of actions for performing some task (reaching some goal) If we have a robot
More informationCommitment Least you haven't decided where to go shopping. Or...suppose You can get milk at the convenience store, at the dairy, or at the supermarket
Planning as Search-based Problem Solving? Imagine a supermarket shopping scenario using search-based problem solving: Goal: buy milk and bananas Operator: buy Heuristic function: does = milk
More informationModule 6. Knowledge Representation and Logic (First Order Logic) Version 2 CSE IIT, Kharagpur
Module 6 Knowledge Representation and Logic (First Order Logic) Lesson 15 Inference in FOL - I 6.2.8 Resolution We have introduced the inference rule Modus Ponens. Now we introduce another inference rule
More informationCreating Admissible Heuristic Functions: The General Relaxation Principle and Delete Relaxation
Creating Admissible Heuristic Functions: The General Relaxation Principle and Delete Relaxation Relaxed Planning Problems 74 A classical planning problem P has a set of solutions Solutions(P) = { π : π
More informationPlanning. Planning. What is Planning. Why not standard search?
Based on slides prepared by Tom Lenaerts SWITCH, Vlaams Interuniversitair Instituut voor Biotechnologie Modifications by Jacek.Malec@cs.lth.se Original slides can be found at http://aima.cs.berkeley.edu
More informationEMPLOYING DOMAIN KNOWLEDGE TO IMPROVE AI PLANNING EFFICIENCY *
Iranian Journal of Science & Technology, Transaction B, Engineering, Vol. 29, No. B1 Printed in The Islamic Republic of Iran, 2005 Shiraz University EMPLOYING DOMAIN KNOWLEDGE TO IMPROVE AI PLANNING EFFICIENCY
More informationIntelligent Systems. Planning. Copyright 2010 Dieter Fensel and Ioan Toma
Intelligent Systems Planning Copyright 2010 Dieter Fensel and Ioan Toma 1 Where are we? # Title 1 Introduction 2 Propositional Logic 3 Predicate Logic 4 Reasoning 5 Search Methods 6 CommonKADS 7 Problem-Solving
More informationArtificial Intelligence
Artificial Intelligence Lecturer 7 - Planning Lecturer: Truong Tuan Anh HCMUT - CSE 1 Outline Planning problem State-space search Partial-order planning Planning graphs Planning with propositional logic
More information== is a decent equivalence
Table of standard equiences 30/57 372 TABLES FOR PART I Propositional Logic Lecture 2 (Chapter 7) September 9, 2016 Equiences for connectives Commutativity: Associativity: P Q == Q P, (P Q) R == P (Q R),
More informationKnowledge Representation. What is knowledge representation?
Knowledge Representation What is knowledge representation? Structured knowledge base Search and inference algorithms 1 Examples of knowledge representation in AI Logic for general reasoning Expert systems
More informationSTABILITY AND PARADOX IN ALGORITHMIC LOGIC
STABILITY AND PARADOX IN ALGORITHMIC LOGIC WAYNE AITKEN, JEFFREY A. BARRETT Abstract. Algorithmic logic is the logic of basic statements concerning algorithms and the algorithmic rules of deduction between
More informationcis32-ai lecture # 21 mon-24-apr-2006
cis32-ai lecture # 21 mon-24-apr-2006 today s topics: logic-based agents (see notes from last time) planning cis32-spring2006-sklar-lec21 1 What is Planning? Key problem facing agent is deciding what to
More informationLECTURE 3: DEDUCTIVE REASONING AGENTS
Agent Architectures LECTURE 3: DEDUCTIVE REASONING AGENTS An Introduction to MultiAgent Systems http://www.csc.liv.ac.uk/~mjw/pubs/imas An agent is a computer system capable of flexible autonomous action
More informationWhat is On / Off Policy?
What is On / Off Policy? Q learns how to perform optimally even when we are following a non-optimal policy In greedy, leaves no trace in Q SARSA is on-policy Learns the best policy given our systematic
More informationSTRIPS HW 1: Blocks World
STRIPS HW 1: Blocks World Operator Precondition Delete List Add List Stack(x, y) CLEAR(y) CLEAR(y) HOLDING(x) HOLDING(x) ON(x, y) Unstack(x, y) ON(x, y) ON(x, y) HOLDING(x) CLEAR(x) CLEAR(y) PickUp(x)
More informationReasoning in Attempto Controlled English: Non-Monotonicity
Reasoning in Attempto Controlled English: Non-Monotonicity Norbert E. Fuchs Department of Informatics & Institute of Computational Linguistics University of Zurich fuchs@ifi.uzh.ch http://attempto.ifi.uzh.ch/
More informationCSC2108: Automated Verification Assignment 1 - Solutions
8 CSC218: Automated Verification Assignment 1 - Solutions 1. Solve the following problem: Use the definition of between states and CTL formulas to explain why means that is true infinitely often along
More informationComputation Club: Gödel s theorem
Computation Club: Gödel s theorem The big picture mathematicians do a lot of reasoning and write a lot of proofs formal systems try to capture the ideas of reasoning and proof in a purely mechanical set
More informationLogic Programming and Reasoning about Actions
Chapter 4 Logic Programming and Reasoning about Actions [Chitta Baral & Michael Gelfond] In this chapter we discuss how recent advances in logic programming can be used to represent and reason about actions
More informationCSE 3402: Intro to Artificial Intelligence Planning
CSE 3402: Intro to Artificial Intelligence Planning Readings: Russell & Norvig 3 rd edition Chapter 10 (in 2 nd edition, Sections 11.1, 11.2, and 11.4) 1 CWA Classical Planning. No incomplete or uncertain
More informationSet 7: Predicate logic Chapter 8 R&N. ICS 271 Fall 2015
Set 7: Predicate logic Chapter 8 R&N ICS 271 Fall 2015 Outline New ontology objects, relations, properties, functions New Syntax Constants, predicates, properties, functions New semantics meaning of new
More informationModule 6. Knowledge Representation and Logic (First Order Logic) Version 2 CSE IIT, Kharagpur
Module 6 Knowledge Representation and Logic (First Order Logic) 6.1 Instructional Objective Students should understand the advantages of first order logic as a knowledge representation language Students
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 19 Tuesday, April 3, 2018 1 Introduction to axiomatic semantics The idea in axiomatic semantics is to give specifications
More informationLecture Overview. CSE3309 Artificial Intelligence. The lottery paradox. The Bayesian claim. The lottery paradox. A Bayesian model of rationality
CSE3309 Lecture Overview The lottery paradox A Bayesian model of rationality Lecture 15 Planning Dr. Kevin Korb School of Computer Science and Software Eng. Building 75 (STRIP), Rm 117 korb@csse.monash.edu.au
More informationPLANNING and INTELLIGENT SYSTEMS
PLANNING and INTELLIGENT SYSTEMS An Introductory Overview by Michail G. Lagoudakis TR-96 2 1 Submitted to Dr. Kimon P. Valavanis The Center of Advanced Computer Studies University of Southwestern Louisiana
More informationNotes for Chapter 12 Logic Programming. The AI War Basic Concepts of Logic Programming Prolog Review questions
Notes for Chapter 12 Logic Programming The AI War Basic Concepts of Logic Programming Prolog Review questions The AI War How machines should learn: inductive or deductive? Deductive: Expert => rules =>
More informationAutomata Theory for Reasoning about Actions
Automata Theory for Reasoning about Actions Eugenia Ternovskaia Department of Computer Science, University of Toronto Toronto, ON, Canada, M5S 3G4 eugenia@cs.toronto.edu Abstract In this paper, we show
More informationPlanning. Why not standard search? What is Planning. Planning language. Planning 1. Difficulty of real world problems
Planning Based on slides prepared by Tom Lenaerts SWITCH, Vlaams Interuniversitair Instituut voor Biotechnologie Modifications by Jacek.Malec@cs.lth.se Original slides can be found at http://aima.cs.berkeley.edu
More informationProgramming Languages Third Edition
Programming Languages Third Edition Chapter 12 Formal Semantics Objectives Become familiar with a sample small language for the purpose of semantic specification Understand operational semantics Understand
More informationCom S 541. Programming Languages I
Programming Languages I Lecturer: TA: Markus Lumpe Department of Computer Science 113 Atanasoff Hall http://www.cs.iastate.edu/~lumpe/coms541.html TR 12:40-2, W 5 Pramod Bhanu Rama Rao Office hours: TR
More informationTowards a Logical Reconstruction of Relational Database Theory
Towards a Logical Reconstruction of Relational Database Theory On Conceptual Modelling, Lecture Notes in Computer Science. 1984 Raymond Reiter Summary by C. Rey November 27, 2008-1 / 63 Foreword DB: 2
More informationPreface Violine(stradivari1) 4string(stradivari1). x.violine(x) 4string(x)
circumscription Preface- FOL We have KB consist the sentence: Violine(stradivari1) We want to conclude it have 4 strings, there are exactly 2 possibilities. One is to add the explicit sentence: 4string(stradivari1).
More informationThe STRIPS Subset of PDDL for the Learning Track of IPC-08
The STRIPS Subset of PDDL for the Learning Track of IPC-08 Alan Fern School of Electrical Engineering and Computer Science Oregon State University April 9, 2008 This document defines two subsets of PDDL
More informationFormal Predicate Calculus. Michael Meyling
Formal Predicate Calculus Michael Meyling May 24, 2013 2 The source for this document can be found here: http://www.qedeq.org/0_04_07/doc/math/qedeq_formal_logic_v1.xml Copyright by the authors. All rights
More informationDipartimento di Elettronica Informazione e Bioingegneria. Cognitive Robotics. SATplan. Act1. Pre1. Fact. G. Gini Act2
Dipartimento di Elettronica Informazione e Bioingegneria Cognitive Robotics SATplan Pre1 Pre2 @ 2015 Act1 Act2 Fact why SAT (satisfability)? 2 Classical planning has been observed as a form of logical
More informationCS 1571 Introduction to AI Lecture 17. Planning. CS 1571 Intro to AI. Planning
S 1571 Introduction to I Lecture 17 Planning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square S 1571 Intro to I Planning Planning problem: find a sequence of actions that achieves some goal an instance
More informationCS61A Lecture 38. Robert Huang UC Berkeley April 17, 2013
CS61A Lecture 38 Robert Huang UC Berkeley April 17, 2013 Announcements HW12 due Wednesday Scheme project, contest out Review: Program Generator A computer program is just a sequence of bits It is possible
More informationModeling Dynamic Domains with ConGolog
Modeling Dynamic Domains with ConGolog Yves Lespérance, Dept. of Computer Science, York University, Toronto, ON Canada, M3J 1P3 lesperan@cs.yorku.ca Todd G. Kelley, John Mylopoulos, and Eric S.K. Yu Dept.
More informationHoare Logic. COMP2600 Formal Methods for Software Engineering. Rajeev Goré
Hoare Logic COMP2600 Formal Methods for Software Engineering Rajeev Goré Australian National University Semester 2, 2016 (Slides courtesy of Ranald Clouston) COMP 2600 Hoare Logic 1 Australian Capital
More informationArtificial Intelligence Sheet II
Artificial Intelligence Sheet II D W Murray Hilary 1993 Q1 (a) Write the following blocks world axioms into Conjunctive Normal Form: x y [ On(x,y) Above(x,y)] x y z [Above(x,y) Above(y,z) Above(x,z)] x
More informationPrinciples of AI Planning. Principles of AI Planning. 7.1 How to obtain a heuristic. 7.2 Relaxed planning tasks. 7.1 How to obtain a heuristic
Principles of AI Planning June 8th, 2010 7. Planning as search: relaxed planning tasks Principles of AI Planning 7. Planning as search: relaxed planning tasks Malte Helmert and Bernhard Nebel 7.1 How to
More informationSearch vs. planning. Planning. Search vs. planning contd. Outline
Search vs. planning Planning onsider the task get milk, bananas, and a cordless drill Standard search algorithms seem to fail miserably: Go To Pet Store Talk to Parrot uy a Dog Go To School Go To lass
More informationIntelligent Agents. State-Space Planning. Ute Schmid. Cognitive Systems, Applied Computer Science, Bamberg University. last change: 14.
Intelligent Agents State-Space Planning Ute Schmid Cognitive Systems, Applied Computer Science, Bamberg University last change: 14. April 2016 U. Schmid (CogSys) Intelligent Agents last change: 14. April
More informationPlanning. What is Planning. Why not standard search? Planning 1
Planning Based on slides prepared by Tom Lenaerts SWITCH, Vlaams Interuniversitair Instituut voor Biotechnologie Modifications by Jacek.Malec@cs.lth.se Original slides can be found at http://aima.cs.berkeley.edu
More informationPlanning as Satisfiability: Boolean vs non-boolean encodings
Planning as Satisfiability: Boolean vs non-boolean encodings Matthew J. Slack Final Year Project Report BSc. Computer Science! Department of Computer Science The University of York 15 th March 2000-1 -
More informationPlanning. Artificial Intelligence 1: Planning. What is Planning. General language features. Planning language. Difficulty of real world problems
Planning Artificial Intelligence 1: Planning Lecturer: Tom Lenaerts SWITCH, Vlaams Interuniversitair Instituut voor Biotechnologie The Planning problem Planning with State-space search Partial-order planning
More informationSemantics. There is no single widely acceptable notation or formalism for describing semantics Operational Semantics
There is no single widely acceptable notation or formalism for describing semantics Operational Describe the meaning of a program by executing its statements on a machine, either simulated or actual. The
More informationAutomated Planning. Plan-Space Planning / Partial Order Causal Link Planning
Automated Planning Plan-Space Planning / Partial Order Causal Link Planning Jonas Kvarnström Automated Planning Group Department of Computer and Information Science Linköping University Partly adapted
More information! Use of formal notations. ! in software system descriptions. ! for a broad range of effects. ! and varying levels of use. !
What Are Formal Methods? David S. Rosenblum ICS 221 Winter 2001! Use of formal notations! first-order logic, state machines, etc.! in software system descriptions! system models, constraints, specifications,
More informationCOMP3411: Artificial Intelligence 8. First Order Logic
COMP9414/9814/3411 16s1 First Order Logic 1 COMP3411: Artificial Intelligence 8. First Order Logic Overview First Order Logic Universal and Existential Quantifiers Russell & Norvig, Chapter 8. Fun with
More informationX-KIF New Knowledge Modeling Language
Proceedings of I-MEDIA 07 and I-SEMANTICS 07 Graz, Austria, September 5-7, 2007 X-KIF New Knowledge Modeling Language Michal Ševčenko (Czech Technical University in Prague sevcenko@vc.cvut.cz) Abstract:
More information3.4 Deduction and Evaluation: Tools Conditional-Equational Logic
3.4 Deduction and Evaluation: Tools 3.4.1 Conditional-Equational Logic The general definition of a formal specification from above was based on the existence of a precisely defined semantics for the syntax
More informationHoare triples. Floyd-Hoare Logic, Separation Logic
Hoare triples Floyd-Hoare Logic, Separation Logic 1. Floyd-Hoare Logic 1969 Reasoning about control Hoare triples {A} p {B} a Hoare triple partial correctness: if the initial state satisfies assertion
More informationPdOd Kev Events I Re-world war I1 rwa
I PdOd Kev Events I Re-world war I rwa LECTURE: Knowledge Representation Overview 0 'Qpes of knowledge: objects, events, meta-knowledge, etc. 0 Characteristics of representation: expressive adequacy vs.
More informationChapter 3. Describing Syntax and Semantics
Chapter 3 Describing Syntax and Semantics Chapter 3 Topics Introduction The General Problem of Describing Syntax Formal Methods of Describing Syntax Attribute Grammars Describing the Meanings of Programs:
More informationAXIOMS OF AN IMPERATIVE LANGUAGE PARTIAL CORRECTNESS WEAK AND STRONG CONDITIONS. THE AXIOM FOR nop
AXIOMS OF AN IMPERATIVE LANGUAGE We will use the same language, with the same abstract syntax that we used for operational semantics. However, we will only be concerned with the commands, since the language
More informationHOL DEFINING HIGHER ORDER LOGIC LAST TIME ON HOL CONTENT. Slide 3. Slide 1. Slide 4. Slide 2 WHAT IS HIGHER ORDER LOGIC? 2 LAST TIME ON HOL 1
LAST TIME ON HOL Proof rules for propositional and predicate logic Safe and unsafe rules NICTA Advanced Course Forward Proof Slide 1 Theorem Proving Principles, Techniques, Applications Slide 3 The Epsilon
More informationAND-OR GRAPHS APPLIED TO RUE RESOLUTION
AND-OR GRAPHS APPLIED TO RUE RESOLUTION Vincent J. Digricoli Dept. of Computer Science Fordham University Bronx, New York 104-58 James J, Lu, V. S. Subrahmanian Dept. of Computer Science Syracuse University-
More informationZ Notation. June 21, 2018
Z Notation June 21, 2018 1 Definitions There are many different ways to introduce an object in a Z specification: declarations, abbreviations, axiomatic definitions, and free types. Keep in mind that the
More informationLecture 4: January 12, 2015
32002: AI (First Order Predicate Logic, Interpretation and Inferences) Spring 2015 Lecturer: K.R. Chowdhary Lecture 4: January 12, 2015 : Professor of CS (VF) Disclaimer: These notes have not been subjected
More informationInterpretations and Models. Chapter Axiomatic Systems and Incidence Geometry
Interpretations and Models Chapter 2.1-2.4 - Axiomatic Systems and Incidence Geometry Axiomatic Systems in Mathematics The gold standard for rigor in an area of mathematics Not fully achieved in most areas
More informationTest 1, Spring 2013 ( Solutions): Provided by Jeff Collins and Anil Patel. 1. Axioms for a finite AFFINE plane of order n.
Math 532, 736I: Modern Geometry Test 1, Spring 2013 ( Solutions): Provided by Jeff Collins and Anil Patel Part 1: 1. Axioms for a finite AFFINE plane of order n. AA1: There exist at least 4 points, no
More informationCSE 215: Foundations of Computer Science Recitation Exercises Set #4 Stony Brook University. Name: ID#: Section #: Score: / 4
CSE 215: Foundations of Computer Science Recitation Exercises Set #4 Stony Brook University Name: ID#: Section #: Score: / 4 Unit 7: Direct Proof Introduction 1. The statement below is true. Rewrite the
More informationClassical Planning Problems: Representation Languages
jonas.kvarnstrom@liu.se 2017 Classical Planning Problems: Representation Languages History: 1959 3 The language of Artificial Intelligence was/is logic First-order, second-order, modal, 1959: General
More informationType Systems COMP 311 Rice University Houston, Texas
Rice University Houston, Texas 1 Type Systems for Programming Language were invented by mathematicians before electronic computers were invented. What is a type? A meaningful subset of the set of the domain
More informationFirst-Order Logic PREDICATE LOGIC. Syntax. Terms
First-Order Logic PREDICATE LOGIC Aim of this lecture: to introduce first-order predicate logic. More expressive than propositional logic. Consider the following argument: all monitors are ready; X12 is
More informationPlanning. Introduction to Planning. Failing to plan is planning to fail! Major Agent Types Agents with Goals. From Problem Solving to Planning
Introduction to Planning Planning Failing to plan is planning to fail! Plan: a sequence of steps to achieve a goal. Problem solving agent knows: actions, states, goals and plans. Planning is a special
More informationAn Evolution of Mathematical Tools
An Evolution of Mathematical Tools From Conceptualization to Formalization Here's what we do when we build a formal model (or do a computation): 0. Identify a collection of objects/events in the real world.
More informationFUZZY BOOLEAN ALGEBRAS AND LUKASIEWICZ LOGIC. Angel Garrido
Acta Universitatis Apulensis ISSN: 1582-5329 No. 22/2010 pp. 101-111 FUZZY BOOLEAN ALGEBRAS AND LUKASIEWICZ LOGIC Angel Garrido Abstract. In this paper, we analyze the more adequate tools to solve many
More informationCIS 194: Homework 8. Due Wednesday, 8 April. Propositional Logic. Implication
CIS 194: Homework 8 Due Wednesday, 8 April Propositional Logic In this section, you will prove some theorems in Propositional Logic, using the Haskell compiler to verify your proofs. The Curry-Howard isomorphism
More informationWhat is AI? Knowledge-based systems in Bioinformatics, 1MB602, Definitions of AI. Acting humanly: Turing test. AI techniques. Intelligent agents
What is I? Knowledge-based systems in ioinformatics, 1M602, 2007 or rtificial Intelligence for ioinformatics making a machine behave in ways that would be called intelligent if a human were so behaving
More informationLEARNING AND EXECUTING GENERALIZED ROBOT PLAS. Richard E. Fikes Peter E. Hart. Nils J. Nilsson. Artificial Intelligence Center.
July 1972 LEARNING AND EXECUTING GENERALIZED ROBOT PLAS Richard E. Fikes Peter E. Hart Nils J. Nilsson Artificial Intelligence Center Technical Note 70 SRI Project 1530 The research reported the Advanced
More informationAxiomatic Specification. Al-Said, Apcar, Jerejian
Axiomatic Specification Al-Said, Apcar, Jerejian 1 Axioms: Wffs that can be written down without any reference to any other Wffs. Wffs that are stipulated as unproved premises for the proof of other wffs
More informationSection 2.2: Introduction to the Logic of Quantified Statements
Section 2.2: Introduction to the Logic of Quantified Statements In this section, we shall continue to examine some of the fundamentals of predicate calculus. Specifically, we shall look at the negations
More informationPropositional Calculus
Propositional Calculus Proposition is a statement that is either or. Example 1 Propositions: It rains. Sun is shining and my coat is wet. If Ann plays with me, I give her a candy. x > 10 x = 1 and y
More information