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7 - Women in Muslim universities: Guardians of tradition or actors of change?
- 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 335-405
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Summary
In their report on the ‘social, economic and educational status of the Muslim community in India’, the Sachar Committee (2004–2006) noted:
Women in general are the torchbearers of community identity. So, when community identity is seen to be under siege, it naturally affects women in dramatic ways. Women, sometimes of their own volition, sometimes because of community pressure, adopt visible markers of community identity on their person and in their behaviour. Their lives, morality, and movement in public spaces are under constant scrutiny and control.
The members of the committee thus hinted at the importance of context in defining women's role vis-à-vis their community. They suggested that Muslim women are more likely to act as guardians of community identity—either out ‘of their own volition’ or ‘because of community pressure'—at a time when large sections of India's Muslim population feel discriminated against. By adopting ‘visible markers’, women come to embody a community identity to be protected from external interference. In this type of context, any attack against women's visibly Muslim markers quickly comes to be seen as an attack upon the entire community. The recent row on the hijab ban in Karnataka (2022) is a good case in point.
Projecting women as guardians of community identity reinforces, in turn, the notion of the ‘Muslim woman’. Be it in the media or in political discourses, Muslim women in India and elsewhere are often projected as a homogeneous category—‘oppressed and often shrouded in a stifling burqa’. These images then feed into equally homogenising notions of Islam. To counter these (mis)representations, scholars have for a long time challenged the monolithic conception of the Muslim woman. In her anthropological study in Delhi's Zakir Nagar (near Jamia), Nida Kirmani stresses the need to examine how gender and religious identity ‘continuously form and re-form along with various other identities’, thus highlighting the ‘contextual nature’ of these identities. Before her, already, several scholars insisted on the need to take into account socio-economic factors, such as education or employment, in order to understand the living conditions of Muslim women in India.
Preface
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp xi-xii
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This book is meant for the serious practitioner-to-be of constructing intelligent machines. Machines that are aware of the world around them, that have goals to achieve, and the ability to imagine the future and make appropriate choices to achieve those goals. It is an introduction to a fundamental building block of artificial intelligence (AI). As the book shows, search is central to intelligence.
Clearly AI is not one monolithic algorithm but a collection of processes working in tandem, an idea espoused by Marvin Minsky in his book The Society of Mind (1986). Human problem solving has three critical components. The ability to make use of experiences stored in memory; the ability to reason and make inferences from what one knows; and the ability to search through the space of possibilities. This book focuses on the last of these. In the real world we sense the world using vision, sound, touch, and smell. An autonomous agent will need to be able to do so as well. Language, and the written word, is perhaps a distinguishing feature of the human species. It is the key to communication which means that human knowledge becomes pervasive and is shared with future generations. The development of mathematical sciences has sharpened our understanding of the world and allows us to compute probabilities over choices to take calculated risks. All these abilities and more are needed by an autonomous agent.
Can one massive neural network be the embodiment of AI? Certainly, the human brain as a seat of intelligence suggests that. Everything we humans do has its origin in activity in our brains, which we call the mind. Perched on the banks of a stream in the mountains we perceive the world around us and derive a sense of joy and well-being. In a fit of contented creativity, we may pen an essay or a poem using our faculty of language. We may call a friend on the phone and describe the scene around us, allowing the friend to visualize the serene surroundings. She may reflect upon her own experiences and recall a holiday she had on the beach. You might start humming your favourite song and then be suddenly jolted out of your reverie remembering that friends are coming over for dinner. You get up and head towards your home with cooking plans brewing in your head.
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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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!
2 - Sifting Sir Syed’s legacy: From the ‘arsenal of Muslim India’ to a symbol of India’s national integration?
- 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 90-136
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To me the work at Aligarh signified no less than the handling at one of the most significant centres of the chief problem with which Indian statesmanship is faced—the problem of an integrated nationhood in a secular democratic state…. The despondent Muslim masses are scattered all over the country. We can dissipate the efforts to revive hope and faith in them. But if we do something significant at Aligarh it can electrify them.
—Zakir Husain to Rajendra Prasad, 19 July 19501After independence, the Indian government tended to project JMI primarily as an experimental institution à la Gandhi, focused on basic education and social reform. Although religion played a central part in JMI's ethos, the government was more likely to compare JMI to Visva Bharati, Tagore's experimental school in rural Bengal, than to AMU. AMU, by contrast, appeared to be the Muslim university par excellence. For many Muslims, it was a source of pride and a symbol of Indian Muslim culture. The institution epitomised Sayyid Ahmad Khan's efforts to uplift the community and preserve the legacy of the glorious Mughal past. However, due to the widespread support of teachers and students for the Muslim League in the 1940s, the university also came to be seen, in certain quarters, as a symbol of Muslim separatism. Long after the riots had ceased, it remained a lieu de mémoire of partition, crystallising resentment against Muslims’ supposedly communal and disloyal attitude.
Despite this prejudice, Zakir Husain strongly believed that AMU could contribute, more than JMI, to the development of an ‘integrated nationhood’. It was precisely because of its legacy as a centre of Muslim politics and educational reform that the university could, he believed, channel the efforts to ‘revive hope and faith’ among the ‘despondent Muslim masses’ and help them feel part of India's ‘secular democratic state’.
A few Congress leaders, particularly Nehru and Azad, shared a similar vision of the university's mission in post-independence India. In 1951, AMU became, along with Banaras Hindu University (BHU), one of the three central universities under the control of the central government. For Nehru, it was essential to ensure that, despite partition, Indian Muslims would feel part of the Indian nation in order to build a secular stat
6 - Bastions of Islam: The defence of Islam as a narrative of empowerment and contestation
- 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 284-334
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In her ethnographic survey conducted in Aligarh city in the mid-1980s, the anthropologist Elisabeth Mann noted:
Islam nowadays has the potential to serve as a rallying point for those who see themselves as betrayed by their elites, persecuted for the creation of the two Pakistans in 1947, suspected by a growing Hindu chauvinistic militancy, and taken advantage of by unscrupulous and cynical politicians.
Mann was quick to remind the reader that tensions remained rife between Aligarh's Muslim elites and non-elites. However, she also recognised that external pressures—the constant suspicion of their loyalty since partition and, increasingly in the 1980s, the sharp rise of Hindu communalism—reinforced a sense of collective identity among co-religionists. In this context, she suggested that invoking Islam could serve as a ‘refuge for the persecuted’. What this chapter will argue is that it could also serve as a language of contestation and empowerment in a context perceived as increasingly hostile.
The 1980s saw a resurgence of communal tensions in India, fed by the development of identity politics. Following the Congress’ crushing defeat in 1977, political competition intensified at the centre, boosting opposition parties that spoke the language of caste or religion to mobilise their constituencies. Although these evolutions were already under way by the 1960s, it was mostly after the emergency that they became prominent at the national level, leading to a shift in norms from national unity to group-based interests in the mainstream political discourse. Other domestic and transnational evolutions accentuated communal tensions. Within India, reports of Muslims’ demographic growth enhanced a sense of insecurity among some sections of the Hindu population. So too did the rise of Islamic fundamentalism, particularly in neighbouring Pakistan under Zia-ul-Haq. The boom of Gulf economies added to these tensions as part of the Hindu population feared that oil money may fund mass conversions to Islam and ‘give to Islamism in India a new glow of self-confidence in one sudden sweep’. These evolutions fed into the ‘vulnerability syndrome’ of the majority population that boosted the rise of the Hindu right.
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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References
- Deepak Khemani, IIT Madras, Chennai
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- 01 November 2025, pp 449-460
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Introduction
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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- 01 November 2025, pp 1-34
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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.
Appendix
- 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 422-423
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1. Nizam of Hyderabad, 10 lakhs (lkhs)
2. Nawab of Bahawalpur, 2 lkhs
3. Nawab of Bhopal, 1 lkh
4. Ruler of Khairpur, 1 lkh
5. Aga Khan, 1 lkh
6. Holiness Mulla Tahir Saifuddin, 1 lkh
7. Ruler of Junagadh, 1 lkh
8. Nawab of Malerkotla, 50,000
9. Maharaja of Darbhanga, 50,000
10. Maharaja of Kashmere, 25,000
11. Maharaja of Bikaner, 20,000
12. Maharaja of Jodhpur, 50,000
13. Bhikampur Estate, Aligarh, 25,000
14. Ruler of Zanjira, 15,000
15. Ruler of Mangrol (Kathiawar), 22,000
16. Mahraja of Ratlam, 10,000
17. Lady Haroon, Karachi, 10,000
18. Raja Saheb Jahangirabad, 10,000
19. Hafiz Md Siddq Sb Rais, Cawnpore, 50,000
20. Haji Md Hamza Sb Rais, Cawnpore, 10,000
21. Ali Janab Nawab Saheb, Dojana, 10,000
22. Nawab of Tonk, 20,000
23. Haji Shah Md Din Sb Rais, Gujrar, 20,000
24. Sir Sorab Saklatvala, Chairman, Dorabji Tata Trust, Bombay, 10,000
25. Mr. Rusi Mistri, Bombay, 25,000 (plus one ring worth 60,000)
26. Haji Habib Tar Md Janoo, Bombay, 10,000
27. Haji Uusuf Sueleman Botawala Charities, Bombay, 25,000
28. Haji Abdul Wahab Sb, Delhi, 10,000
29. Mr. Ispahani Calcutta, 25,000
30. S.A. Latif, Calcutta, 10,000
31. Mr. Ferozuddin, Calcutta, 10,000
32. K.S. Wachal Molla, Calcutta, 10,000
33. Seth Adamji Haji Daud, Calcutta, 25,000
34. S.M. Hanif, Calcutta, 25,000
35. Dawood Yakoob Gandhi, Calcutta, 25,000
36. G.A. Randeria Ltd, Calcutta, 10,000
37. A.G. Mohammad, Calcutta, 10,000
38. Mahboob Chowdhury, Calcutta, 10,000
39. S. Rehman, Calcutta, 10,000
40. Sueleman Chowdhury, 11,000
41. K.B. Farid Ad Chowhury (sic) Chittagong, 25,000
42. Ismail Haji Isa Sait, Cochin, 15,000
43. M.K. Macker Pillay Esq, Alwaya, 15,00
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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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.
Bibliography
- Laurence Gautier, Centre de Sciences Humaines
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List of tables
- Laurence Gautier, Centre de Sciences Humaines
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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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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.
6 - Algorithm A* and Variations
- Deepak Khemani, IIT Madras, Chennai
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- Search Methods in Artificial Intelligence
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- 01 November 2025, pp 147-184
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Finding a solution is one aspect of problem solving. Executing it is another. In certain applications the cost of executing the solution is important. For example, maintaining supplies to the International Space Station, a repetitive task, or sending a rocket to Jupiter, an infrequent activity. Coming down to Earth, the manufacturing industry needs to manage its supplies, inventory, scheduling, and shipping of products. At home, juggling the morning activity of cooking, sending off kids to school, and heading for office after grabbing a coffee and a bite could do with optimized processes.
In this chapter we look at the algorithm A* for finding optimal solutions. It is a heuristic search algorithm that guarantees an optimal solution. It does so by combining the goal seeking of best first search with a tendency to keep as close to the source as possible. We begin by looking at the algorithm branch & bound that focuses only on the latter, before incorporating the heuristic function.
We revert to graph search for the study of algorithms that guarantee optimal solutions. The task is to find a shortest path in a graph from a start node to a goal node. We have already studied algorithms BFS and DFID in Chapter 3. The key idea there was to extend that partial path which was the shortest. We begin with the same strategy. Except that now we add weights to edges in the graph. Without edge weights, the optimal or shortest path has the least number of edges in the path. With edge weights added, we modify this notion to the sum of the weights on the edges.
The common theme continuing in our search algorithms is as follows:
Pick the best node from OPEN and extend it, till you pick the goal node.
The question that remains is the definition of ‘best’. In DFS, the deepest node is the best node. In BestFirstSearch, the node that appears to be closest to the goal is the best. In BFS, the node closest to the start node is the best. We begin by extending the idea behind breadth first search.
We can generalize our common theme as follows. With every node N on OPEN, we associate a number that stands for the estimated cost of the final solution.
A note on transliteration and translation
- Laurence Gautier, Centre de Sciences Humaines
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- Between Nation and ‘Community'
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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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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.
Index
- Laurence Gautier, Centre de Sciences Humaines
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Frontmatter
- Laurence Gautier, Centre de Sciences Humaines
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2 - Search Spaces
- Deepak Khemani, IIT Madras, Chennai
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- 01 November 2025, pp 29-46
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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.