1234731 results in Books
List of figures
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp xi-xii
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Frontmatter
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp i-vi
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4 - Resisting minority politics, holding on to composite nationalism: Jamia Millia Islamia in the post-Nehruvian period
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp 189-228
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Summary
The campaign for AMU's minority status propelled the university to the forefront of Muslim politics in the 1960s and 1970s, making AMU's status one of the key ‘Muslim issues’ of the period. By pressing for the recognition of Muslims’ minority rights, the campaign revealed the limits of the so-called Nehruvian consensus. It highlighted the difficulty of transcending religion-based differences in a context where notions of majority and minority continued to mediate conceptions of the nation—among the population as well as among state actors—and to shape state policies on the ground.
Yet the demands for religious minority rights continued to suffer from a ‘justificatory deficit’: many state actors continued to see them as threats to the nation's unity and to its secular Constitution. In this context, Muslim groups at AMU and JMI sought alternative, more legitimate discursive frameworks to claim support from the state and to defend their conceptions of the nation and citizenship. The following chapters will examine the different discursive frameworks that emerged within and around Muslim universities in response to the rise of minority politics in the post-Nehruvian period. In this way, the book questions the simplistic notion that secular nationalist politics gradually gave way—from the 1960s onwards—to communal identity-based politics. To start with, the book has shown in the preceding chapters that there was no consensus around the secular nationalist discourse, even under Nehru. The next chapters will highlight the different forms of resistance to minority politics that developed within Muslim universities in the subsequent period. These resistances did not usually come from a purely areligious standpoint. As we will see, they often stemmed from competing—yet sometimes overlapping— understandings of Muslim identity. We may argue, drawing inspiration from Barbara Metcalf, that Indian Muslimness ‘offered a wide range of orientations, not one single stance’. The comparison between AMU and JMI further allows us to highlight differences of rhythm in the evolution of the dominant discursive frameworks in these two institutions. Muslim politics was neither monolithic nor did it unfold in a homogeneous time. One therefore has to bear in mind the differences in institutional cultures, proximity to power, regional anchorage and visibility in the public sphere to account for the diachronic evolutions at AMU and JMI.
Introduction
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp 1-34
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Summary
Can a Muslim university be an Indian university? In his landmark article ‘Can a Muslim Be an Indian?’ Gyanendra Pandey draws a revealing comparison between two common expressions—Hindu nationalists and nationalist Muslims. While Hindus are considered to be ‘natural’ Indians, who are nationalist by default—Hindu nationalism being one brand of nationalism— Indian Muslims are taken to be primarily Muslims, whatever their political stance may be. Unlike Hindus, their commitment to the nation cannot be taken for granted; it has to be proven, for their Muslimness casts doubt on their Indianness.
Similar apprehensions affect Muslim institutions, including universities. By Muslim universities, I refer to institutions established by Muslim individuals or organisations, primarily—though not exclusively—for Muslim students. Unlike madrasas, these universities offer mostly non-religious education along the same lines as other non-Muslim universities. Therefore, their ‘Muslim’ character rests on their foundation's history and on their Muslim-majority population, much more than on their educational programmes. Visible Islamic symbols, such as mosques or tombs, may act as reminders of this character; so too can students, teachers and administrators’ frequent allusions to the need to preserve and promote ‘Muslim culture’. However, there is no consensus on either the interpretation of ‘Muslim culture’ among university members or how and to what extent it should frame life on campus.
For many external observers, there seems to be a fundamental tension between these universities’ Muslim character and their capacity, or even their willingness to serve the nation. These apprehensions, inherited from partition, surfaced again recently during the debates around the Citizenship Amendment Act (CAA). In December 2019, a wave of protests broke out across India when the parliament adopted this Act, which introduced, for the first time, a religious criterion in the rules of access to Indian citizenship. On 15 December, amidst growing student mobilisation, police forces stormed into two of India's prime universities—Jamia Millia Islamia (JMI) and Aligarh Muslim University (AMU). These two institutions had one clear common denominator: they were both Muslim universities. For part of the press and the political body, this was reason enough to suspect a ‘jihadi’ influence behind students’ protests.
3 - Blind Search
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 47-74
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Summary
In this chapter we introduce the basic machinery needed for search. We devise algorithms for navigating the implicit search space and look at their properties. One distinctive feature of the algorithms in this chapter is that they are all blind or uninformed. This means that the way the algorithms search the space is always the same irrespective of the problem instance being solved.
We look at a few variations and analyse them on the four parameters we defined in the last chapter: completeness, quality of solution, time complexity, and space complexity. We observe that complexity becomes a stumbling block, as our principal foe CombEx inevitably rears its head. We end by making a case for different approaches to fight CombEx in the chapters that follow.
In the last chapter we looked at the notion of search spaces. Search spaces, as shown in Figure 2.2, are trees corresponding to the different traversals possible in the state space or the solution space. In this chapter we begin by constructing the machinery, viz. algorithms, for navigating this space. We begin our study with the corresponding tiny state space shown in Figure 3.1.
The tiny search problem has seven nodes, including the start node S, the goal node G, and five other nodes named A, B, C, D, and E. Without any loss of generality, let us assume that the nodes are states in a state space. The algorithms apply to the solution space as well. The left side of the figure describes the MoveGen function with the notation Node → (list of neighbours). On the right side is the corresponding graph which, remember, is implicit and not given upfront. The algorithm itself works with the MoveGen function and also the GoalTest function. The latter, for this example, simply knows that state G is the goal node. For configuration problems like the N-queens, it will need to inspect the node given as the argument.
The search space that an algorithm explores is implicit. It is generated on the fly by the MoveGen function, as described in Algorithm 2.1. The candidates generated are added to what is traditionally called OPEN, from where they are picked one by one for inspection. In this chapter we represent OPEN as a list data structure.
4 - Heuristic Search
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 75-114
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Summary
Having introduced the machinery needed for search in the last chapter, we look at approaches to informed search. The algorithms introduced in the last chapter were blind, or uninformed, taking no cognizance at all of the actual problem instance to be solved and behaving in the same bureaucratic manner wherever the goal might be. In this chapter we introduce the idea of heuristic search, which uses domain specific knowledge to guide exploration. This is done by devising a heuristic function that estimates the distance to the goal for each candidate in OPEN.
When heuristic functions are not very accurate, search complexity is still exponential, as revealed by experiments. We then investigate local search methods that do not maintain an OPEN list, and study gradient based methods to optimize the heuristic value.
Knowledge is necessary for intelligence. Without knowledge, problem solving with search is blind. We saw this in the last chapter. In general, knowledge is that sword in the armoury of a problem solver that can cut through the complexity. Knowledge accrues over time, either distilled from our own experiences or assimilated from interaction with others – parents, teachers, authors, coaches, and friends. Knowledge is the outcome of learning and exists in diverse forms, varying from tacit to explicit. When we learn to ride a bicycle, we know it but are unable to articulate our knowledge. We are concerned with explicit knowledge. Most textbook knowledge is explicit, for example, knowing how to implement a leftist heap data structure.
In a well known incident from ancient Greece, it is said that Archimedes, considered by many to be the greatest scientist of the third century BC, ran naked onto the streets of Syracuse. King Hieron II was suspicious that a goldsmith had cheated him by adulterating a bar of gold given to him for making a crown. He asked Archimedes to investigate without damaging the crown. Stepping into his bathtub Archimedes noticed the water spilling out, and realized in a flash that if the gold were to be adulterated with silver, then it would displace more water since silver was less dense. This was his epiphany moment when he discovered what we now know as the Archimedes principle. And he ran onto the streets shouting ‘Eureka, eureka!’ We now call such an enlightening moment a Eureka moment!
12 - Constraint Satisfaction
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 393-440
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Summary
What is common between solving a sudoku or a crossword puzzle and placing eight queens on a chessboard so that none attacks another? They are all problems where each number or word or queen placed on the board is not independent of the others. Each constrains some others. Like a piece in a jigsaw puzzle that must conform to its neighbours. Interestingly, all these puzzles can be posed in a uniform formalism, constraints. The constraints must be respected by the solution – the constraints must be satisfied. And a unified representation admits general purpose solvers. This has given rise to an entire community engaged in constraint processing. Constraint processing goes beyond constraint satisfaction, with variations concerned with optimization. And it is applicable on a vast plethora of problems, some of which have been tackled by specialized algorithms like linear programming and integer programming.
In this chapter we confine ourselves to finite domain constraint satisfaction problems (CSPs) and study different approaches to solving them. We highlight the fact that CSP solvers can combine search and logical inferences in a flexible manner.
A constraint network R or a CSP is a triple,
R = <X, D, C>
where X is a set of variable names, D is a set of domains, one for each variable, and C is a set of constraints on some subsets of variables (Dechter, 2003). We will use the names X = ﹛x1, x2, …, xn﹜ where convenient with the corresponding domains D = ﹛D1, D2, …, Dn﹜. The domains can be different for each variable and each domain has values that the variable can take, Di = ﹛ai1, ai2, …, aik﹜. Let C = ﹛C1, C2, …, Cm﹜ be the constraints. Each constraint Ci has a scope Si R X and a relation Ri that is a subset of the cross product of the domains of the variables in Si. Based on the size of Si, we will refer to the constraints as unary, binary, ternary, and so on. A CSP is often depicted by a constraint graph and a matching diagram, as described in the examples to follow.
We will confine ourselves to finite domain CSPs, in which the domain of each variable is discrete and finite. We will also specify the relations in extensional form well suited for our algorithms.
5 - Stochastic Local Search
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 115-146
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Summary
Search spaces can be huge. The number of choices faced by a search algorithm can grow exponentially. We have named this combinatorial explosion, the principal adversary of search, CombEx. In Chapter 4 we looked at one strategy to battle CombEx, the use of knowledge in the form of heuristic functions – knowledge that would point towards the goal node. Yet, for many problems, such heuristics are hard to acquire and often inadequate, and algorithms continue to demand exponential time.
In this chapter we introduce stochastic moves to add an element of randomness to search. Exploiting the gradient deterministically has its drawbacks when the heuristic functions are imperfect, as they often are. The steepest gradient can lead to the nearest optimum and end there. We add a tendency of exploration, which could drag search away from the path to local optima.
We also look at the power of many for problem solving, as opposed to a sole crusader. Population based methods have given a new dimension to solving optimization problems.
Douglas Hofstadter says that humans are not known to have a head for numbers (Hofstadter, 1996). For most of us, the numbers 3.2 billion and 5.3 million seem vaguely similar and big. A very popular book (Gamow, 1947) was titled One, Two, Three … Infinity. The author, George Gamow, talks about the Hottentot tribes who had the only numbers one, two, and three in their vocabulary, and beyond that used the word many. Bill Gates is famously reputed to have said, ‘Most people overestimate what they can do in one year and underestimate what they can do in ten years.’
So, how big is big? Why are computer scientists wary of combinatorial growth? In Table 2.1 we looked at the exponential function 2N and the factorial N!, which are respectively the sizes of search spaces for SAT and TSP, with N variables or cities. How long will take it to inspect all the states when N = 50?
For a SAT problem with 50 variables, 250 = 1,125,899,906,842,624. How big is that? Let us say we can inspect a million or 106 nodes a second. We would then need 1,125,899,906.8 seconds, which is about 35.7 years! There are N! = 3.041409320 × 1064 non-distinct tours (each distinct tour has 2N representations) of 50 cities.
Contents
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp v-x
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2 - Search Spaces
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 29-46
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Summary
In this chapter we lay the foundations of problem solving using first principles. The first principles approach requires that the agent represent the domain in some way and investigate the consequences of its actions by simulating the actions on these representations. The representations are often referred to as models of the domain and the simulations as search. This approach is also known as model based reasoning, as opposed to problem solving using memory or knowledge, which, incidentally, has its own requirements of searching over representations, but at a sub-problem solving retrieval level.
We begin with the notion of a state space and then look at the notion of search spaces from the perspective of search algorithms. We characterize problems as planning problems and configuration problems, and the corresponding search spaces that are natural to them. We also present two iconic problems, the Boolean satisfiability problem (SAT) and the travelling salesman problem (TSP), among others.
In this chapter we lay the foundations of the search spaces that an agent would explore.
First, we imagine the space of possibilities. Next, we look at a mechanism to navigate this space. And then in the chapters that follow we figure out what search strategy an algorithm can use to do so efficiently.
Our focus is on creating domain independent solvers, or agents, which can be used to solve a variety of problems. We expect that the users of our solvers will implement some domain specific functions in a specified form that will create the domain specific search space for our domain independent algorithms to search in. In effect, these domain specific functions create the space, which our algorithm will view as a graph over which to search. But the graph is not supplied to the search algorithm upfront. Rather, it is constructed on the fly during search. This is done by the user supplied neighbourhood function that links a node in this graph to its neighbours, generating them when invoked. The neighbourhood function takes a node as an input and computes, or returns, the set of neighbours in the abstract graph for the search algorithm to search in.
8 - Chess and Other Games
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 221-262
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Summary
Acting rationally in a multi-agent scenario has long been studied under the umbrella of games. Game theory is a study of decision making in the face of other players, usually adversaries of the given player or agent. Economists study games to understand the behaviour of governments and corporates when everyone has the goal of maximizing their own payoffs. A stark example is the choice of NATO countries refusing to act directly against the Russian invasion of Ukraine given the threat of nuclear escalation.
In this chapter we turn our attention to the simplified situation in which the agent has one adversary. Board games like chess exemplify this scenario and have received considerable attention in the world of computing. In such games each player makes a move on her turn, and the information is complete since both players can see the board, and where the outcome is a win for one and a loss for the other. We look at the most popular algorithms for playing board games.
Chess has long fascinated humankind as a game of strategy and skill. It was probably invented in India in the sixth century in the Gupta empire when it was known as chaturanga. A comprehensive account of its history was penned in 1913 by H.J.R. Murray (2015). The name refers to the four divisions an army may have. The infantry includes the pawns, the knights make up the cavalry, the rooks correspond to the chariotry, and the bishops the elephantry (though the Hindi word for the piece calls it a camel). In Persia the name was shortened to chatrang. This in turn transformed to shatranj as exemplified in the 1924 story by Munshi Premchand (2020) and the film of the same name by Satyajit Ray, Shatranj Ke Khiladi (The Chess Players). It became customary to warn the king by uttering shāh (the Persian word for king) which became check, and the word mate came from māt which means defeated. Checkmate is derived from shāh māt which says that the king has been vanquished.
Table 8.1 lists the names of the chess pieces in Sanskrit, Persian, Arabic, and English (Murray, 2015). In Hindi users often say oont (camel) for bishop and haathi (elephant) for rook.
From India the game spread to Persia, and then to Russia, Europe, and East Asia around the ninth century.
Index
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp 458-467
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List of tables
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 01 November 2025, pp xiii-xiv
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List of abbreviations
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp xxi-xxii
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Acknowledgements
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp xiii-xiv
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References
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 449-460
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1 - Introduction
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 1-28
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We will adopt the overall goal of artificial intelligence (AI) to be ‘to build machines with minds, in the full and literal sense’ as prescribed by the Canadian philosopher John Haugeland (1985).
Not to create machines with a clever imitation of human-like intelligence. Or machines that exhibit behaviours that would be considered intelligent if done by humans – but to build machines that reason.
This book focuses on search methods for problem solving. We expect the user to define the goals to be achieved and the domain description, including the moves available with the machine. The machine then finds a solution employing first principles methods based on search. A process of trial and error. The ability to explore different options is fundamental to thinking.
As we describe subsequently, such methods are just amongst the many in the armoury of an intelligent agent. Understanding and representing the world, learning from past experiences, and communicating with natural language are other equally important abilities, but beyond the scope of this book. We also do not assume that the agent has meta-level abilities of being self-aware and having goals of its own. While these have a philosophical value, our goal is to make machines do something useful, with as general a problem solving approach as possible.
This and other definitions of what AI is do not prescribe how to test if a machine is intelligent. In fact, there is no clear-cut universally accepted definition of intelligence. To put an end to the endless debates on machine intelligence that ensued, the brilliant scientist Alan Turing proposed a behavioural test.
Can Machines Think?
Ever since the possibility of building intelligent machines arose, there have been raging debates on whether machine intelligence is possible or not. All kinds of arguments have been put forth both for and against the possibility. It was perhaps to put an end to these arguments that Alan Turing (1950) proposed his famous imitation game, which we now call the Turing Test. The test is simply this: if a machine interacts with a human using text messages and can fool human judges a sufficiently large fraction of times that they are chatting with another human, then we can say that the machine has passed the test and is intelligent.
7 - Problem Decomposition
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 185-220
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So far our approach to solving problems has been characterized by state space search. We are in a given state, and we have a desired or goal state. We have a set of moves available to us which allow us to navigate from one state to another. We search through the possible moves, and we employ a heuristic function to explore the space in an informed manner. In this chapter we study two different approaches to problem solving.
One, with emphasis on knowledge that we can acquire from domain experts. We look at mechanisms to harness and exploit such knowledge. In the last century in the 1980s, an approach to express knowledge in the form of if–then rules gained momentum, and many systems were developed under the umbrella of expert systems. Although only a few lived up to expert level expectations, the technology matured into an approach to allow human users to impart their knowledge into systems. The key to this approach was the Rete algorithm that allowed an inference engine to efficiently match rules with data.
The other looks at problem solving from a teleological perspective. That is, we look at a goal based approach which investigates what needs to be done to achieve a goal. In that sense, it is reasoning backwards from the goal. We look at how problems can be formulated as goal trees, and an algorithm AO* to solve them.
The search algorithms we have studied so far take a holistic view of a state representing the given situation. In practice, states are represented in some language in which the different constituents are described. The state description is essentially a set of statements. As the importance of knowledge for problem solving became evident, using rules to spot patterns in the description and proposing actions emerged as a problem solving strategy.
Pattern Directed Inference Systems
An approach to problem solving that was developed in the mid-1970s was called pattern directed inference systems (Waterman and Hayes-Roth, 1978). The basic idea is that patterns in a given state are associated with actions.
Appendix: Algorithm and Pseudocode Conventions
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- By S. Baskaran
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 441-448
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The algorithms presented in this book assume eager evaluation. The values of primitive types (integers, reals, strings) are passed by value, and tuples, lists, arrays, sets, stacks, queues, etc., are passed by reference, similar to how Java treats primitive values and objects.
The data structures (container types) like sets, arrays, stacks and queues, and the operations on those structures carry their usual meaning, and their usages in the algorithms are self explanatory.
Tuple
A tuple is an ordered collection of fixed number of elements, where each element may be of a different type. A tuple is represented as a comma separated sequence of elements, surrounded by parenthesis.
tuple → ( ELEMENT 1 , ELEMENT 2 , … , ELEMENT k)
A tuple of two elements is called a pair, for example, (S, null), ((A, S), 1), (S, [A, B]) are pairs. And a tuple of three elements is called a triple, for example, (S, null, 0), (A, S, 1), (S, A, B) are triples. A tuple of k elements is called a k-tuple, for example, (S, MAX, −∞, ∞), (A, MIN, LIVE, ∞, 42).
Note: parenthesis is also used to indicate precedence, like in (3+1) * 4 or in (1 : (4 : [ ])), its usage will be clear from the context.
List
A list is an ordered collection of an arbitrary number of elements of the same type. A list is read from left to right and new elements are added at the left end. Lists are constructed recursively like in Haskell.
list → ELEMENT : list
list → [ ]
The ‘:’ operator is a list constructor; it takes an element (HEAD) and a list (TAIL) and constructs a new list (HEAD : TAIL) similar to cons(HEAD, TAIL) in LISP. Using head:tail notation, a list such as [3, 1, 4] is recursively constructed from (3 : (1 : (4 : [ ]))), similar to cons(3, cons(1, cons(4, nil))) in LISP. The empty list [ ] has no head or tail.
5 - Uplifting backward Muslims: The new consensus?
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 15 April 2024
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- 01 November 2025, pp 229-283
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The University shall have the powers … to promote especially the educational and cultural advancement of the Muslims of India.
—AMU (Amendment) Act (1981)The way the traditional Muslim leaders reacted to the Babri Masjid issue made me think that there was an urgent need for the Dalit Muslims to come up with their own leaders, championing bread-and-butter concerns rather than emotional and symbolic issues…. Our first concern should be jobs and education for our people … the traditional Muslim leadership has been championing merely symbolic issues, be it the cause of Urdu, the minority character of the Aligarh Muslim University, Muslim Personal Law or the Babri Masjid.
—Ejaz Ali, leader of the All-India Backward Muslim MorchaThe demolition of the Babri Masjid in December 1992 constituted for many Indian Muslims, as well as for a great many other Indian citizens, a shocking landmark moment in the history of the republic. For Mushirul Hasan, Muslims’ response to this ‘cataclysmic event’ was one of ‘anger, indignation and disbelief ‘. The demolition laid bare the state's failure to uphold the rule of law and to protect its minority population from the attacks—physical and symbolic—of self-legitimising Hindutva forces. For many, this was a devastating blow not only against Muslims but against the very idea of India as a secular democracy.
This was also a moment of reckoning for Muslim leaders. A number of individuals and organisations called for a radical transformation of Muslim politics. In their minds, established Muslim leaders were partly to blame for this ‘cataclysm’. By focusing on divisive ‘emotive and symbolic issues’, they had contributed to the sharp rise of communal tensions and failed to protect Muslims. The demolition of the Babri Masjid thus prompted the emergence of new backward Muslim (pasmanda) organisations, such as Ejaz Ali's All-India Backward Muslim Morcha, which rejected these ‘traditional Muslim leaders’ and their claim to speak for all Muslims. The ‘emotive and symbolic issues’ that they stood for (including AMU's status) mattered to Muslim elites alone, they suggested. These organisations instead called pasmanda Muslims to ‘come up with their own leaders’ to champion ‘bread-and-butter issues’.