Reflections on Chapter 710
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Reflections on Chapter 710
Write the reflection on the following chapters:
 Chapter 7
 Chapter 8
 Chapter 9
 Chapter 10
blacksnake Posts : 18
Join date : 20101120
Age : 30
Location : Davao
Re: Reflections on Chapter 710
Chapter 7: First Order Logic
Chapter 8: Building A Knowledge Base
Chapter 9: Inference in FirstOrder Logic
Chapter 10: Logical Reasoning Systems
Finally, after a brief explaination on about builging agents in reasoning systems, this chapter introduces the concepts on Logical Reasoning Systems. It reveals the deeper concept on store and fetch since it was introduces on one of my subject around last quarter of 2008.
The store and fetch procedures are now implemented through sentences and terms. Indexing also applies too which also involves the use of predicate logic.
This chapter also introduces the logic programming systems like Prolog. It also explains the deeper side in Inheritance and concepts on semantic networks.
Since that, it reveals the four major classes of logical reasoning systems: Logic programming systems and theorem provers, Production Systems, Semantic Networks, Description logics.
This chapter is a generalpurpose representation language that is based on an ontological commitment to the existence of objects and relations in the world.
This chapter experiences the applications on Chapter 6. This shows the way in dealing with sentence and symantics in which it will give the logical meaning to the sentence below.
Those illustrations taken from the basics in Discrete Mathematics which shows the logical equivalent of those values shown on those illustrations.
This chapter experiences the applications on Chapter 6. This shows the way in dealing with sentence and symantics in which it will give the logical meaning to the sentence below.
Those illustrations taken from the basics in Discrete Mathematics which shows the logical equivalent of those values shown on those illustrations.
Chapter 8: Building A Knowledge Base
This chapter shows the properties and facts on designing a Good and Bad Knowledge Bases. Also it illustrates the comparison of steps between Knowledge Engineering and Programming. This also details on setting a reasoning skills which interconnects AI.
Chapter 9: Inference in FirstOrder Logic
This chapter also connects with Chapter 6 with the definition of notion of inference. It also applies the concepts of inference and could be achieve through a propositional logic. The algorithms introduced in this chapter like backward and forward chaining algorithm, the inference resolution and the like. Other algorithms like:
These theories explain in this chapter also creates a direct meaning of the sentence. It was also introduced Ontology.
 Unification
 Modus Ponens
 Horn form
 Generalized resolution: conjunctive and implicative normal form.
These theories explain in this chapter also creates a direct meaning of the sentence. It was also introduced Ontology.
Chapter 10: Logical Reasoning Systems
Finally, after a brief explaination on about builging agents in reasoning systems, this chapter introduces the concepts on Logical Reasoning Systems. It reveals the deeper concept on store and fetch since it was introduces on one of my subject around last quarter of 2008.
The store and fetch procedures are now implemented through sentences and terms. Indexing also applies too which also involves the use of predicate logic.
This chapter also introduces the logic programming systems like Prolog. It also explains the deeper side in Inheritance and concepts on semantic networks.
Since that, it reveals the four major classes of logical reasoning systems: Logic programming systems and theorem provers, Production Systems, Semantic Networks, Description logics.
blacksnake Posts : 18
Join date : 20101120
Age : 30
Location : Davao
Re: Reflections on Chapter 710
Chapter 7 talks about how firstorder logic can be used as the representation language for a knowledgebased agent. As we all know that a knowledgebased agent is an intelligent agent which is program to act as an assistance to human task. This is particularly applied to an agent since logic's basis is more on proofs than hypothesis. In other words, in order to sustain the people's inconsistencies, an agent must be as intelligent as humans, such that it needs to: react what it perceives; extract abstract descriptions of the current state from percepts; maintain an internal model of relevant aspects of the world that are not directly available from percepts; express and use information about the desirability of actions in various circumstances; and use goals in conjunction with knowledge about actions to construct plans.
Chapter 8 really taught me facts about knowledge. It states there that "One cannot understand knowledge representation without doing it, or at least seeing it" to which I found true. For sure, anyone will agree. Since people are just human beings, knowledge is only there way to acquire intelligence and build up one's consciousness. It so happen that the first time humans saw light on their childhood years, one's mind merely was trying to understand on what has been seen, then an answer suddenly strikes one's mind and finally concluded that what has been seen was purely a light. In other words, an experience is just one key to acquire knowledge. On the other hand, if knowledge is represented then irrelevant detail must be eliminated since this is not cover within the scope. " Debugging problem is often simpler than debugging programs", I just find this phrase to be generally interesting especially I use to debug programs when I wanted to trace some of my errors and correct it in order for the program to function accurately. I would say that maybe problem is easier to debug if only this is not that critical compare to your programs and vice versa.
Chapter 9 merely focuses more on the logical side or an analysis of logical inference in firstorder logic and its algorithm. I just find it difficult because this base more on proofs wherein you need to have an innate understanding of particular things and weighing in certain differences and similarities before arriving on a conclusion. There are many forms being use in logical inferencing such as unification, Modus Ponens, Horn Form, resolution, conjunctive normal form and implicative normal form. Unification is particularly made during variable substitution in which it eliminates the 5 instantation step in firstorder proofs to make the process more efficient. Modus Ponens, otherwise, uses the unification to provide a strong inference rule. Unfortunately, Horn Form is not a complete proof system whereas resolution is. The latter requires a normal form but only sentence can be put into the form. Moreover, it work with either conjuctive normal form and implicative normal form.
Chapter 10 falls on the logical reasoning systems, this would mean I think of how it acquire its intelligence by becoming an expert of logical reasoning. Well, of course, there are techniques and characteristics being consider namely logical programming system and theorem proves wherein it uses inferencing, propositional logic etc. as an application to the system; Production systems, how much time and what type of services it can offer to its valued user; Semantic Networks, how userfriendly it is and Description Logic, how it will respond or react to things. Definitely, this system came in existence because there are people that wanted to make the work easier for everybody. In other words, ones that have gifted knowledge turns out conscious or generous on his or her community and it is not a bad thing!
Chapter 8 really taught me facts about knowledge. It states there that "One cannot understand knowledge representation without doing it, or at least seeing it" to which I found true. For sure, anyone will agree. Since people are just human beings, knowledge is only there way to acquire intelligence and build up one's consciousness. It so happen that the first time humans saw light on their childhood years, one's mind merely was trying to understand on what has been seen, then an answer suddenly strikes one's mind and finally concluded that what has been seen was purely a light. In other words, an experience is just one key to acquire knowledge. On the other hand, if knowledge is represented then irrelevant detail must be eliminated since this is not cover within the scope. " Debugging problem is often simpler than debugging programs", I just find this phrase to be generally interesting especially I use to debug programs when I wanted to trace some of my errors and correct it in order for the program to function accurately. I would say that maybe problem is easier to debug if only this is not that critical compare to your programs and vice versa.
Chapter 9 merely focuses more on the logical side or an analysis of logical inference in firstorder logic and its algorithm. I just find it difficult because this base more on proofs wherein you need to have an innate understanding of particular things and weighing in certain differences and similarities before arriving on a conclusion. There are many forms being use in logical inferencing such as unification, Modus Ponens, Horn Form, resolution, conjunctive normal form and implicative normal form. Unification is particularly made during variable substitution in which it eliminates the 5 instantation step in firstorder proofs to make the process more efficient. Modus Ponens, otherwise, uses the unification to provide a strong inference rule. Unfortunately, Horn Form is not a complete proof system whereas resolution is. The latter requires a normal form but only sentence can be put into the form. Moreover, it work with either conjuctive normal form and implicative normal form.
Chapter 10 falls on the logical reasoning systems, this would mean I think of how it acquire its intelligence by becoming an expert of logical reasoning. Well, of course, there are techniques and characteristics being consider namely logical programming system and theorem proves wherein it uses inferencing, propositional logic etc. as an application to the system; Production systems, how much time and what type of services it can offer to its valued user; Semantic Networks, how userfriendly it is and Description Logic, how it will respond or react to things. Definitely, this system came in existence because there are people that wanted to make the work easier for everybody. In other words, ones that have gifted knowledge turns out conscious or generous on his or her community and it is not a bad thing!
Honey Lynne Accion Posts : 8
Join date : 20101130
Re: Reflections on Chapter 710
Chapter 7: FirstOrder Logic
The chapter focuses how firstorder logic, a generalpurpose representation language that is based on an ontological commitment to the existence of objects and relations in the world, can be utilizes as the representation language for a knowledgebased agent. Constant symbols and predicate symbols name objects and relations, respectively using function symbols. Atomic sentence consists of a predicate applied to one or more terms. Complex sentences use connectives like propositional logic and quantified sentences allowing the expression of general rules. Using firstorder logic, an agent that reasons can be defined wherein it needs to: react to what it perceives; extract abstract descriptions of the current state from percepts; maintain an internal model of relevant aspects of the world that are not directly available from percepts; express and use information about the desirability of actions in various circumstances; use goals in conjunction with knowledge about actions to construct plans. Using the conventions of situation calculus, the knowledge about the actions and their effects can be characterized in which this knowledge enables the agent to keep track of the world and to assume the effects of plans of action. Causal rules are often more flexible and entail a wider range of consequences, but can be more expensive to use in inference.
Chapter 8: Building a Knowledge Base
In this chapter shows the process of representing knowledge of a domain goes through several stages. Starting with the informal stage which involves deciding what kinds of objects and relations need to be represented or what we called ontology. After that a vocabulary is chosen, and employ to encode general knowledge of the domain. After encoding specific problem instances, automated inference procedures can be used to solve them. Good representations eliminate irrelevant detail, capture relevant distinctions, and express knowledge at the most general level possible. Constructing knowledgebased systems has advantages over programming. Specialpurpose ontologies, such as the one constructed for the circuits domain, can be effective within the domain but often need to be generalized to broaden their coverage. A generalpurpose ontology needs to cover a wide variety of knowledge, and should be capable in principle of handling any domain. This chapter presented a general ontology based around categories and the event calculus and covered structured objects, time and space, change, processes, substances, and beliefs. It also presented a detailed analysis of the shopping domain, exercising the general ontology and showing how the domain knowledge can be used by a shopping agent. Lastly, it is important remembering that the nature of a good representation depends on the world being represented.
Chapter 9: Inference in FirstOrder Logic
In this chapter, an analysis of logical inference in firstorder logic is presented and a number of algorithms for doing it. A simple extension of the prepositional inference rules allows the construction of proofs for firstorder logic. Unfortunately, the branching factor for the quantifier is huge. The use of unification to identify appropriate substitutions for variables eliminates the 5 instantiation step in firstorder proofs, making the process much more efficient. A generalized version of Modus Ponens uses unification to provide a natural and powerful I inference rule, which can be used in a backwardchaining or forwardchaining algorithm. The canonical form for Modus Ponens is Horn form: p\ A ... A pn => q, where pt and q are atoms. This form cannot represent all sentences, and Modus Ponens is not a complete proof system. The generalized resolution inference rule provides a complete system for proof by refutation. It requires a normal form, but any sentence can be put into the form.
Chapter 10: Logical Reasoning Systems
This chapter gives an association between the conceptual foundations of knowledge representation and reasoning and the practical world of actual reasoning systems. It highlights that real understanding of these systems can only be obtained by trying them out. It has explains the implementation techniques and characteristics of four major classes of logical reasoning systems: (1) Logic programming systems and theorem provers; (2) Production systems; (3) Semantic networks; (4) Description logics. It also has seen that there is an exchange between the expressiveness of the system and its efficiency. Compilation can provide significant improvements in efficiency by taking advantage of the fact that the set of sentences is fixed in advance. Usability is enhanced by providing a clear semantics for the representation language, and by simplifying the execution model so that the user has a good idea of the computations required for inference.
The chapter focuses how firstorder logic, a generalpurpose representation language that is based on an ontological commitment to the existence of objects and relations in the world, can be utilizes as the representation language for a knowledgebased agent. Constant symbols and predicate symbols name objects and relations, respectively using function symbols. Atomic sentence consists of a predicate applied to one or more terms. Complex sentences use connectives like propositional logic and quantified sentences allowing the expression of general rules. Using firstorder logic, an agent that reasons can be defined wherein it needs to: react to what it perceives; extract abstract descriptions of the current state from percepts; maintain an internal model of relevant aspects of the world that are not directly available from percepts; express and use information about the desirability of actions in various circumstances; use goals in conjunction with knowledge about actions to construct plans. Using the conventions of situation calculus, the knowledge about the actions and their effects can be characterized in which this knowledge enables the agent to keep track of the world and to assume the effects of plans of action. Causal rules are often more flexible and entail a wider range of consequences, but can be more expensive to use in inference.
Chapter 8: Building a Knowledge Base
In this chapter shows the process of representing knowledge of a domain goes through several stages. Starting with the informal stage which involves deciding what kinds of objects and relations need to be represented or what we called ontology. After that a vocabulary is chosen, and employ to encode general knowledge of the domain. After encoding specific problem instances, automated inference procedures can be used to solve them. Good representations eliminate irrelevant detail, capture relevant distinctions, and express knowledge at the most general level possible. Constructing knowledgebased systems has advantages over programming. Specialpurpose ontologies, such as the one constructed for the circuits domain, can be effective within the domain but often need to be generalized to broaden their coverage. A generalpurpose ontology needs to cover a wide variety of knowledge, and should be capable in principle of handling any domain. This chapter presented a general ontology based around categories and the event calculus and covered structured objects, time and space, change, processes, substances, and beliefs. It also presented a detailed analysis of the shopping domain, exercising the general ontology and showing how the domain knowledge can be used by a shopping agent. Lastly, it is important remembering that the nature of a good representation depends on the world being represented.
Chapter 9: Inference in FirstOrder Logic
In this chapter, an analysis of logical inference in firstorder logic is presented and a number of algorithms for doing it. A simple extension of the prepositional inference rules allows the construction of proofs for firstorder logic. Unfortunately, the branching factor for the quantifier is huge. The use of unification to identify appropriate substitutions for variables eliminates the 5 instantiation step in firstorder proofs, making the process much more efficient. A generalized version of Modus Ponens uses unification to provide a natural and powerful I inference rule, which can be used in a backwardchaining or forwardchaining algorithm. The canonical form for Modus Ponens is Horn form: p\ A ... A pn => q, where pt and q are atoms. This form cannot represent all sentences, and Modus Ponens is not a complete proof system. The generalized resolution inference rule provides a complete system for proof by refutation. It requires a normal form, but any sentence can be put into the form.
Chapter 10: Logical Reasoning Systems
This chapter gives an association between the conceptual foundations of knowledge representation and reasoning and the practical world of actual reasoning systems. It highlights that real understanding of these systems can only be obtained by trying them out. It has explains the implementation techniques and characteristics of four major classes of logical reasoning systems: (1) Logic programming systems and theorem provers; (2) Production systems; (3) Semantic networks; (4) Description logics. It also has seen that there is an exchange between the expressiveness of the system and its efficiency. Compilation can provide significant improvements in efficiency by taking advantage of the fact that the set of sentences is fixed in advance. Usability is enhanced by providing a clear semantics for the representation language, and by simplifying the execution model so that the user has a good idea of the computations required for inference.
deyong Posts : 7
Join date : 20110124
Reflection (Chapter 710)
Chapter 7: FirstOrder Logic
FirstOrder Logic is a logic that makes a stronger set of ontological commitments. It has been so important to mathematics, philosophy and artificial intelligence precisely because those fields – and indeed, much everyday human existence – can be usefully thought of as dealing with objects and the relations between them. It can also express facts about all of the objects in the universe. Although it commits to the existence of objects and relations, it does not make an ontological commitment to such things as categories, time, and events, which also seem to show up in most facts about the world. Constant symbols and predicate symbols name objects and relations respectively. We have a choice of writing diagnostic rules that reason from percepts to propositions about the world or casual rules that describe how conditions in the world cause percepts to come about.
Chapter 8: Building a Knowledge Base
The process of building a knowledge base is called knowledge engineering. A knowledge engineer is someone who investigates particular domain, determines what concepts are important in that domain, and creates a formal representation of the objects and relations in the domain. One does not become a proficient knowledge engineer just by studying the syntax and semantics of a representation language. It takes a lots of examples before one can develop a good style in any language, be it a language for programming, reasoning, or communicating. To explain the general principles of good design, we need to have an example. Every knowledge base has two potential consumers: human readers and inference procedures. The main advantage of knowledge engineering is that it requires less commitment, and thus less work. The knowledge engineer specifies what is true, and the inference procedure figures out how to turn the facts into a solution to the problem. One cannot understand knowledge representation without doing it, or at least seeing it. Good representations eliminate irrelevant detail, capture relevant distinctions, and express knowledge at the most general level possible. Finally it is worth recalling that the nature of an appropriate representation depends on the world being represented and the intended range of uses of the representation.
Chapter 9: Inference in FirstOrder Logic
Logical inference was studied extensively in Greek mathematics. The type of inference most carefully studied by Aristotle was the syllogism. A simple extension of the propositional inference rules allows the construction of proofs for the firstorder logic. The generalized resolution inference rule provides a complete proof system for the proof by refutation. It requires a normal form, but any sentence can built into the form. Thus, we have serious difficult, in the form of a collection of operators that give long proofs and a large branching factor, and hence a potentially explosive search problem. The canonical form for Modus Ponens mandates that each sentence in the knowledge base be either an atomic sentence or an implication with a conjunction of atomic sentences on the left hand side and a single atom on the right. The generalized Modus Ponens rule can be used into ways; Forwarding chaining and Backward Chaining. Chaining with resolution is more powerful than chaining with Modus Ponens, but it is still not complete.
Chapter 10: Logical Reasoning Systems
It is a good idea to build agents as reasoning systems – systems that explicitly represent and reason with knowledge. The main advantage of this is a high degree of modularity. All knowledgebased systems rely on the fundamental operation of retrieving sentences satisfying certain conditions – for example, finding an atomic sentence that unifies with a query, or finding an implication that has given atomic sentence as one of its premises. Recently, much of the effort in logic programming has been aimed toward increasing efficiency by building information about specific domains or specific inference patterns into the logic programming language.
FirstOrder Logic is a logic that makes a stronger set of ontological commitments. It has been so important to mathematics, philosophy and artificial intelligence precisely because those fields – and indeed, much everyday human existence – can be usefully thought of as dealing with objects and the relations between them. It can also express facts about all of the objects in the universe. Although it commits to the existence of objects and relations, it does not make an ontological commitment to such things as categories, time, and events, which also seem to show up in most facts about the world. Constant symbols and predicate symbols name objects and relations respectively. We have a choice of writing diagnostic rules that reason from percepts to propositions about the world or casual rules that describe how conditions in the world cause percepts to come about.
Chapter 8: Building a Knowledge Base
The process of building a knowledge base is called knowledge engineering. A knowledge engineer is someone who investigates particular domain, determines what concepts are important in that domain, and creates a formal representation of the objects and relations in the domain. One does not become a proficient knowledge engineer just by studying the syntax and semantics of a representation language. It takes a lots of examples before one can develop a good style in any language, be it a language for programming, reasoning, or communicating. To explain the general principles of good design, we need to have an example. Every knowledge base has two potential consumers: human readers and inference procedures. The main advantage of knowledge engineering is that it requires less commitment, and thus less work. The knowledge engineer specifies what is true, and the inference procedure figures out how to turn the facts into a solution to the problem. One cannot understand knowledge representation without doing it, or at least seeing it. Good representations eliminate irrelevant detail, capture relevant distinctions, and express knowledge at the most general level possible. Finally it is worth recalling that the nature of an appropriate representation depends on the world being represented and the intended range of uses of the representation.
Chapter 9: Inference in FirstOrder Logic
Logical inference was studied extensively in Greek mathematics. The type of inference most carefully studied by Aristotle was the syllogism. A simple extension of the propositional inference rules allows the construction of proofs for the firstorder logic. The generalized resolution inference rule provides a complete proof system for the proof by refutation. It requires a normal form, but any sentence can built into the form. Thus, we have serious difficult, in the form of a collection of operators that give long proofs and a large branching factor, and hence a potentially explosive search problem. The canonical form for Modus Ponens mandates that each sentence in the knowledge base be either an atomic sentence or an implication with a conjunction of atomic sentences on the left hand side and a single atom on the right. The generalized Modus Ponens rule can be used into ways; Forwarding chaining and Backward Chaining. Chaining with resolution is more powerful than chaining with Modus Ponens, but it is still not complete.
Chapter 10: Logical Reasoning Systems
It is a good idea to build agents as reasoning systems – systems that explicitly represent and reason with knowledge. The main advantage of this is a high degree of modularity. All knowledgebased systems rely on the fundamental operation of retrieving sentences satisfying certain conditions – for example, finding an atomic sentence that unifies with a query, or finding an implication that has given atomic sentence as one of its premises. Recently, much of the effort in logic programming has been aimed toward increasing efficiency by building information about specific domains or specific inference patterns into the logic programming language.
aqua_pepper214 Posts : 7
Join date : 20110124
Age : 31
Location : Davao City
Re: Reflections on Chapter 710
Chpater 7
In chapter 7 firstorder logic was shown on how it can be used as the presentation language for knowledgebased agent.Firstorder logic is a generalpurpose presentation language that is based on an ontological commitment to the existence of objects and relations in the world. The interpretation specifies what the symbols refer to. Constant symbols and predicate symbols name objects and relations, respectively. Complex terms name objects using function symbols.
It is possible to define an agent that reasons using firstorder logic. Such an agent needs to
1. react to what it perceives;
2. extract abstract descriptions of the current state from percepts;
3. maintain an internal model of relevant aspects of the world that are not directly
available from percepts;
4. express and use information about the desirability of actions in various circumstances;
5. use goals in conjunction with knowledge about actions to construct plans.
Chapter 8
In chapter 8, it is about building a knowledge base. The process of building a knowledge base is called knowledge engineering. The knowledge engineer must understand enough about the domain question to represent the important objects and relationships. He must also understand enough about the representation language to correctly encode these facts. He must also understand enough about the implementation of the inference procedure to assure taht queries can be answered in a reasonable amount of time. There is also a fivestep methodology that can be used to help focus the development of a knowledge base and to integrate the engineer's thinking at the three levels, which are: decide what to talk about; decide on a vocabulary of predicates, functions, and constants; encode general knowledge about the domain; encode a description of the specific problem instance; and, pose queries to the inference procedure and get answers.
Chapter 9
In Chapter 9, it talks about Inference in FirstOrder Logic. It has presented an analysis of logical inference in firstorder logic, and a number of algorithms
for doing it. Three new inference rules were added to handle firstorder logic sentences with quantifiers: Universal Elimination, Existential Elimination, and Existential Introduction. Forward and Backward Chaining were the two ways that the Generalized Modus Ponens rule can be used. Forward Chaining is usually used when
a new fact is added to the database and we want to generate its consequences. Backward Chaining start with something we want to prove, find implication sentences that would allow us to conclude it, and then attempt to establish their premises in turn.
Chapter 10
In chapter 10, it has provided a connection between the conceptual foundations of knowledge representation and reasoning, and the practical world of actual reasoning systems. It was also emphasized in this chapter that real understanding of these systems can only be obtained by trying them out. There are four major classes of logical reasoning system namely: logic programming systems and theorem provers, production systems, semantic networks, and description logics.Logic provers use resolution to prove sentences in full first order logic, often for mathematical and scientific reasoning tasks. Logic programming languages typically restrict the logic, disallowing full treatment of negation, disjunction, and/or equality. They usually use backward chaining. Production Systems use implications as their primary representation. Semantic networks concentrate on categories of objects and the relations between them. Description logics are designed to focus on categories and their definitions.
In chapter 7 firstorder logic was shown on how it can be used as the presentation language for knowledgebased agent.Firstorder logic is a generalpurpose presentation language that is based on an ontological commitment to the existence of objects and relations in the world. The interpretation specifies what the symbols refer to. Constant symbols and predicate symbols name objects and relations, respectively. Complex terms name objects using function symbols.
It is possible to define an agent that reasons using firstorder logic. Such an agent needs to
1. react to what it perceives;
2. extract abstract descriptions of the current state from percepts;
3. maintain an internal model of relevant aspects of the world that are not directly
available from percepts;
4. express and use information about the desirability of actions in various circumstances;
5. use goals in conjunction with knowledge about actions to construct plans.
Chapter 8
In chapter 8, it is about building a knowledge base. The process of building a knowledge base is called knowledge engineering. The knowledge engineer must understand enough about the domain question to represent the important objects and relationships. He must also understand enough about the representation language to correctly encode these facts. He must also understand enough about the implementation of the inference procedure to assure taht queries can be answered in a reasonable amount of time. There is also a fivestep methodology that can be used to help focus the development of a knowledge base and to integrate the engineer's thinking at the three levels, which are: decide what to talk about; decide on a vocabulary of predicates, functions, and constants; encode general knowledge about the domain; encode a description of the specific problem instance; and, pose queries to the inference procedure and get answers.
Chapter 9
In Chapter 9, it talks about Inference in FirstOrder Logic. It has presented an analysis of logical inference in firstorder logic, and a number of algorithms
for doing it. Three new inference rules were added to handle firstorder logic sentences with quantifiers: Universal Elimination, Existential Elimination, and Existential Introduction. Forward and Backward Chaining were the two ways that the Generalized Modus Ponens rule can be used. Forward Chaining is usually used when
a new fact is added to the database and we want to generate its consequences. Backward Chaining start with something we want to prove, find implication sentences that would allow us to conclude it, and then attempt to establish their premises in turn.
Chapter 10
In chapter 10, it has provided a connection between the conceptual foundations of knowledge representation and reasoning, and the practical world of actual reasoning systems. It was also emphasized in this chapter that real understanding of these systems can only be obtained by trying them out. There are four major classes of logical reasoning system namely: logic programming systems and theorem provers, production systems, semantic networks, and description logics.Logic provers use resolution to prove sentences in full first order logic, often for mathematical and scientific reasoning tasks. Logic programming languages typically restrict the logic, disallowing full treatment of negation, disjunction, and/or equality. They usually use backward chaining. Production Systems use implications as their primary representation. Semantic networks concentrate on categories of objects and the relations between them. Description logics are designed to focus on categories and their definitions.
fjsanico Posts : 6
Join date : 20110210
My Reflection on Chapter 7
Chapter 7
This Chapter talks about FirstOrder Logic. Its concepts, applications and its importance in AI. It is a fact that propositional logic is not enough to be used as a representation language. A propositional language has a very limited ontology making it unreliable at times. FirstOrder Logic has a stronger set of ontological commitments. The main component of FirstOrder Logic is objects. Objects in FirstOrder Logic are things with individual identities. Properties is also a component of a FirstOrder Logic which distinguish objects from other objects. This objects are related in many ways and this is called Relations. Relations may be a Function. Syntax and Semantics are also important in FirstOrder Logic.
This Chapter talks about FirstOrder Logic. Its concepts, applications and its importance in AI. It is a fact that propositional logic is not enough to be used as a representation language. A propositional language has a very limited ontology making it unreliable at times. FirstOrder Logic has a stronger set of ontological commitments. The main component of FirstOrder Logic is objects. Objects in FirstOrder Logic are things with individual identities. Properties is also a component of a FirstOrder Logic which distinguish objects from other objects. This objects are related in many ways and this is called Relations. Relations may be a Function. Syntax and Semantics are also important in FirstOrder Logic.
blueacid Posts : 10
Join date : 20110314
Reflections on Chapter 8
Building a Knowledge Base
This Chapter discusses the importance of a knowledge base, concepts supporting the theory behind it and uses for Artificial Intelligence. After reading the book, I have also added something on my Knowledge Base and that is the fact that process of building a knowledge base is called Knowledge Engineering. An engineer who is building a knowledge base must be exposed to different kinds of examples before he can be called a proficient knowledge engineer. An engineer could not build its own knowledge base without first asking the real experts on a specific field. The act of asking the knowledge of the real experts is called Knowledge Acquisition.
This Chapter discusses the importance of a knowledge base, concepts supporting the theory behind it and uses for Artificial Intelligence. After reading the book, I have also added something on my Knowledge Base and that is the fact that process of building a knowledge base is called Knowledge Engineering. An engineer who is building a knowledge base must be exposed to different kinds of examples before he can be called a proficient knowledge engineer. An engineer could not build its own knowledge base without first asking the real experts on a specific field. The act of asking the knowledge of the real experts is called Knowledge Acquisition.
blueacid Posts : 10
Join date : 20110314
Reflection on Chapter 10
Logical Reasoning Systems
In this chapter the concepts in previous chapters are used and integrated or combined to build a Logical Reasoning System. This chapter describes implementation techniques and characteristics of four major classes of logical reasoning systems:
• Logic programming systems and theorem provers.
• Production systems.
• Semantic networks.
• Description logics.
In this chapter the concepts in previous chapters are used and integrated or combined to build a Logical Reasoning System. This chapter describes implementation techniques and characteristics of four major classes of logical reasoning systems:
• Logic programming systems and theorem provers.
• Production systems.
• Semantic networks.
• Description logics.
blueacid Posts : 10
Join date : 20110314
My Reflection On Chapter 9
Inference was first discussed in previous chapters and it is related to Propositional Logic which makes it Propositional Inference. It is also discussed in the previous chapters that propositional logic is not enough to solve complex problems therefore the book suggested to use FirstOrder Logic. FirstOrder Logic may also use Inference in order to gain efficiency.
blueacid Posts : 10
Join date : 20110314
Summary
Chapter 7: FirstOrder Logic
Firstorder logic is a formal logical system used in mathematics, philosophy, linguistics, and computer science. It goes by many names, including: firstorder predicate calculus, the lower predicate calculus, quantification theory, and predicate logic (a less precise term). Firstorder logic is distinguished from propositional logic by its use of quantifiers; each interpretation of firstorder logic includes a domain of discourse over which the quantifiers range. The adjective "firstorder" is used to distinguish firstorder theories from higherorder theories in which there are predicates having other predicates or functions as arguments or in which predicate quantifiers or function quantifiers are permitted or both.In interpretations of firstorder theories predicates are associated with sets. In interpretations of higher order theories they may be also associated with sets of sets.
Chapter 8: Building a Knowledge Base
Constructing knowledgebased systems has advantages over programming: the knowledge engineer has to concentrate only on what's true about the domain, rather than on solving the problems and encoding the solution process; the same knowledge can often be used in several ways; debugging knowledge is often simpler than debugging programs.
Chapter 9: Inference in FirstOrder Logic
Proofs – extend propositional logic inference to deal with quantifiers
• Unification
• Generalized modus ponens
• Forward and backward chaining – inference rules and reasoning program
• Completeness – Gödel’s theorem: for FOL, any sentence entailed by another set of sentences can be proved from that set
• Resolution – inference procedure that is complete for any set of sentences
A simple extension of the prepositional inference rules allows the construction of proofs for firstorder logic. Unfortunately, the branching factor for the quantifier is huge. The use of unification to identify appropriate substitutions for variables eliminates the 5 instantiation step in firstorder proofs, making the process much more efficient.
Chapter 10: Logical Reasoning Systems
Chapter 10 shows the implementation techniques and characteristics of four major classes of logical reasoning systems:
• Logic programming systems and theorem provers  Theorem provers use resolution to prove sentences in full firstorder logic, often for mathematical and scientific reasoning tasks.
• Production systems  use implications as their primary representation
• Semantic networks and Frame Systems  These systems use the metaphor that objects are nodes in a graph, that these nodes are organized in a taxonomic structure, and that links between nodes represent binary relations.
• Description logics  evolved from semantic networks due to pressure
to formalize what the networks mean while retaining the emphasis on taxonomic structure '•
as an organizing principle.
"Usability is enhanced by providing a clear semantics for the representation language, and by simplifying the execution model so that the user has good idea of the computations required for inference."
Firstorder logic is a formal logical system used in mathematics, philosophy, linguistics, and computer science. It goes by many names, including: firstorder predicate calculus, the lower predicate calculus, quantification theory, and predicate logic (a less precise term). Firstorder logic is distinguished from propositional logic by its use of quantifiers; each interpretation of firstorder logic includes a domain of discourse over which the quantifiers range. The adjective "firstorder" is used to distinguish firstorder theories from higherorder theories in which there are predicates having other predicates or functions as arguments or in which predicate quantifiers or function quantifiers are permitted or both.In interpretations of firstorder theories predicates are associated with sets. In interpretations of higher order theories they may be also associated with sets of sets.
Chapter 8: Building a Knowledge Base
Constructing knowledgebased systems has advantages over programming: the knowledge engineer has to concentrate only on what's true about the domain, rather than on solving the problems and encoding the solution process; the same knowledge can often be used in several ways; debugging knowledge is often simpler than debugging programs.
Chapter 9: Inference in FirstOrder Logic
Proofs – extend propositional logic inference to deal with quantifiers
• Unification
• Generalized modus ponens
• Forward and backward chaining – inference rules and reasoning program
• Completeness – Gödel’s theorem: for FOL, any sentence entailed by another set of sentences can be proved from that set
• Resolution – inference procedure that is complete for any set of sentences
A simple extension of the prepositional inference rules allows the construction of proofs for firstorder logic. Unfortunately, the branching factor for the quantifier is huge. The use of unification to identify appropriate substitutions for variables eliminates the 5 instantiation step in firstorder proofs, making the process much more efficient.
Chapter 10: Logical Reasoning Systems
Chapter 10 shows the implementation techniques and characteristics of four major classes of logical reasoning systems:
• Logic programming systems and theorem provers  Theorem provers use resolution to prove sentences in full firstorder logic, often for mathematical and scientific reasoning tasks.
• Production systems  use implications as their primary representation
• Semantic networks and Frame Systems  These systems use the metaphor that objects are nodes in a graph, that these nodes are organized in a taxonomic structure, and that links between nodes represent binary relations.
• Description logics  evolved from semantic networks due to pressure
to formalize what the networks mean while retaining the emphasis on taxonomic structure '•
as an organizing principle.
"Usability is enhanced by providing a clear semantics for the representation language, and by simplifying the execution model so that the user has good idea of the computations required for inference."
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