Clustered superscalar processors parcel practical units into bunches. Information NO COPYING sending inside a bunch works as ordinary permitting subordinate guidelines Inference From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we man infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Common sense knowledge and reasoning This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1970s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed. Learning from experience Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information. Planning Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions This is a study of the kinds of knowledge that are required for solving problems in the world. Ontology Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s. Heuristics A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful. Genetic Programming Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. Search and Control Strategies: Problem solving is an important aspect of Artificial Intelligence. A problem can be considered to consist of a goal and a set of actions that can be taken to lead to the goal. At any given time, we consider the state of the search space to represent where we have reached as a result of the actions we have applied so far. For example, consider the problem of looking for a contact lens on a football field. The initial state is how we start out, which is to say we know that the lens is somewhere on the field, but we don't know where. If we use the representation where we examine the field in units of one square foot, then our first action might be to examine the square in the top-left corner of the field. If we do not find the lens there, we could consider the state now to be that we have examined the top-left square and have not found the lens. After a number of actions, the state might be that we have examined 500 squares, and we have now just found the lens in the last square we examined. This is a goal state because it satisfies the goal that we had of finding a contact lens. Search is a method that can be used by computers to examine a problem space like this in order to find a goal. Often, we want to find the goal as quickly as possible or without using too many resources. A problem space can also be considered to be a search space because in order to solve the problem, we will search the space for a goal state.We will continue to use the term search space to describe this concept. In this chapter, we will look at a number of methods for examining a search space. These methods are called search methods. The Importance of Search in AI It has already become clear that many of the tasks underlying AI can be phrased in terms of a search for the solution to the problem at hand. Many goal based agents are essentially problem solving agents which must decide what to do by searching for a sequence of actions that lead to their solutions. For production systems, we have seen the need to search for a sequence of rule applications that lead to the required fact or action. Consider an operating system that uses hardware support for paging to provide virtual memory to applications. (a) (i) Explain how the hardware and operating system support for paging combine to prevent one process from accessing another's memory. [3 marks] (ii) Explain how space and time overheads arise from use of paging, and how the Translation Lookaside Buffer (TLB) mitigates the time overheads. [3 marks] (b) Consider a system with a five level page table where each level in the page table is indexed by 9 bits and pages are 4 kB in size. A TLB is provided that is indexed by the first 57 bits of the address provided by the process, and achieves a 90% hit rate. A main memory access takes 40 ns while an access to the TLB takes 10 ns. The maximum memory read bandwidth is 100 GB/s. (i) What is the effective memory access latency? [4 marks] (ii) A colleague suggests replacing the system above with one that provides 80 GB/s memory read bandwidth and main memory access latency of 30 ns. Explain whether you should accept the replacement or not, and why. For neural network systems, we need to search for the set of connection weights that will result in the required input to output mapping. Which search algorithm one should use will generally depend on the problem domain? There are four important factors to consider: Completeness - Is a solution guaranteed to be found if at least one solution exists? Optimality - Is the solution found guaranteed to be the best (or lowest cost) solution if there exists more than one solution? Time Complexity - The upper bound on the time required to find a solution, as a function of the complexity of the problem. Space Complexity - The upper bound on the storage space (memory) required at any point during the search, as a function of the complexity of the problem. Preliminary concepts Two varieties of space-for-time algorithms: Input enhancement preprocess the input (or its part) to store some info to be used later in solving the problem o Counting for sorting o String searching algorithms Prestructuring Your partner needs to recruit two additional human annotators to re-mark your preparing information. For what reason could this be really smart, how might you measure arrangement in this undertaking, preprocess the input to make accessing its elements easier o Hashing o Indexing schemes (e.g., B-trees) State Space Representations: The state space is simply the space of all possible states, or configurations, that our system may be in. Generally, of course, we prefer to work with some convenient representation of that search space. There are two components to the representation of state spaces: Static States Transitions between States State Space Graphs: If the number of possible states of the system is small enough, we can represent all of them, along with the transitions between them, in a state space graph, e.g. Routes through State Space: Our general aim is to search for a route, or sequence of transitions, through the state space graph from our initial state to a goal state. Sometimes there will be more than one possible goal state. We define a goal test to determine if a goal state has been achieved. The solution can be represented as a sequence of link labels (or transitions) on the state space graph. Note that the labels depend on the direction moved along the link. Sometimes there may be more than one path to a goal state, and we may want to find the optimal (best possible) path. We can define link costs and path costs for (c) A creative engineer suggests structuring the TLB so that not all the bits of the presented address need match to result in a hit. Suggest how this might be achieved, and what might be the costs and benefits of doing so. [6 marks] to execute on successive clock cycles. Correspondence between groups ordinarily causes an unexpected setback How could this problem be overcome? [2 marks] (d) The designer also wishes the real-time system Demonstrate the beginning image in the two sentence structures. [2 marks] 1. may be utilized to a sentence structure analyser taking a token stream as information (by means of calls to work lex()) and giving as result an theoretical language structure tree relating to part (d). Notice both transcribed also, consequently created grammar analysers. 2. Give a brief and rudimentary clarification of the standards of how the language coming about because of part (b) may be utilized to a sentence structure analyser taking a token stream as information (by means of calls to work lex()) and giving as result an theoretical language structure tree relating to part (d). List the terminal images and non-terminal images, and count the creation rules both in the first syntax and in the sentence structure in your response to part (b). 3. Define a sort or types (in C, Java, or ML) appropriate for holding a theoretical linguistic structure tree coming about because of your solution to part (b).Notice both transcribed also, consequently created grammar analysers. 4. Data Structures and Algorithms (a) Describe how the Lempel Ziv text compression algorithm works, illustrating your answer by deriving the sequence of numbers and corresponding bit patterns it would generate when applied to a string starting with the following 24 characters: ABCDABCDABCDABCDABCDABCD ... You may assume that the initial table is of size 256 (containing bytes 0 to 255) and that the codes for "A", "B", "C" and "D" are 65, 66, 67 and 68, respectively. [12 marks] (b) Estimate how many bits the algorithm would use to encode a string consisting of 12000 repetitions of the character "Asa"

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a i Hardware and Operating System Support for Paging Explain how hardware support for paging such as the Memory Management Unit MMU and operating syst... View the full answer