Other Types of Attacks
This chapter describes the conditions under which LeT carries out terrorist attacks on holidays as well as attempted (but unsuccessful) attacks. The chapter discusses several TP-rules about these types of terror attacks that were derived automatically from the LeT data set used in this book.
Dealing with Lashkar-e-Taiba: A Multi-player Game-Theoretic Perspective
Lashkar-e-Taiba (LET) is one of the deadliest terrorist groups in the world. With over 100 attacks worldwide since 2004, LET has become a political force within Pakistan, a proxy fighting force for the Pakistani Army, and a terror group that can carry out complex, coordinated attacks such as the 2008 Mumbai attacks. In this paper, we develop a game-theoretic analysis of how to deal with LET using a 5-player game whose players include LET, India, the Pakistani military, the (civilian) Pakistani government, and the US. We use an expert on LET and Pakistan to develop a payoff matrix and compute pure and mixed Nash equilibria (NE) in this payoff matrix. We study several of these NEs in detail. Our analysis shows that: (i) there are 6 pure NEs in which LET eliminates its armed wing, (ii) increasing external financial/military support for Pakistan leads to no NEs where LET reduces violence, (iii) almost all NEs in which LET significantly reduces violence involve coordinated actions by both the US and India.
Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds
The semantics of probabilistic logic programs (PLPs) is usually given through a possible worlds semantics. We propose a variant of PLPs called action probabilistic logic programs or -programs that use a two-sorted alphabet to describe the conditions under which certain real-world entities take certain actions. In such applications, worlds correspond to sets of actions these entities might take. Thus, there is a need to find the most probable world (MPW) for -programs. In contrast, past work on PLPs has primarily focused on the problem of entailment. This paper quickly presents the syntax and semantics of -programs and then shows a naive algorithm to solve the MPW problem using the linear program formulation commonly used for PLPs. As such linear programs have an exponential number of variables, we present two important new algorithms, called $ \textsf{HOP} $ and $ \textsf{SemiHOP} $ to solve the MPW problem exactly. Both these algorithms can significantly reduce the number of variables in the linear programs. Subsequently, we present a “binary” algorithm that applies a binary search style heuristic in conjunction with the Naive, $ \textsf{HOP} $ and $ \textsf{SemiHOP} $ algorithms to quickly find worlds that may not be “most probable.” We experimentally evaluate these algorithms both for accuracy (how much worse is the solution found by these heuristics in comparison to the exact solution) and for scalability (how long does it take to compute). We show that the results of $ \textsf{SemiHOP} $ are very accurate and also very fast: more than 1030,000 worlds can be handled in a few minutes. Subsequently, we develop parallel versions of these algorithms and show that they provide further speedups.