A hybrid algorithm for coalition structure generation

The current state-of-the-art algorithm for optimal coalition structure generation is IDP-IP—an algorithm that combines IDP (a dynamic programming algorithm due to Rahwan and Jennings, 2008b) with IP (a tree-search algorithm due to Rahwan et al., 2009). In this paper we analyse IDP-IP, highlight its limitations, and then develop a new approach for combining IDP with IP that overcomes these limitations

Coalition structure generation over graphs

We give the analysis of the computational complexity of coalition structure generation over graphs. Given an undirected graph G = (N,E) and a valuation function v : P(N) → R over the subsets of nodes, the problem is to find a partition of N into connected subsets, that maximises the sum of the components values. This problem is generally NP-complete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected members — that is, two nodes have no effect on each others marginal contribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minor free graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive linear time bounds for graphs of bounded treewidth. However, as we show, the problem remains NP-complete for planar graphs, and hence, for any Kk minor free graphs where k ≥ 5. Moreover, a 3-SAT problem with m clauses can be represented by a coalition structure generation problem over a planar graph with O(m2) nodes. Importantly, our hardness result holds for a particular subclass of valuation functions, termed edge sum, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph

Improving location prediction services for new users with probabilistic latent semantic analysis

Location prediction systems that attempt to determine the mobility patterns of individuals in their daily lives have become increasingly common in recent years. Approaches to this prediction task include eigenvalue decomposition [5], non-linear time series analysis of arrival times [10], and variable order Markov models [1]. However, these approachesall assume sufficient sets of training data. For new users, by definition, this data is typically not available, leading to poor predictive performance. Given that mobility is a highly personal behaviour, this represents a significant barrier to entry. Against this background, we present a novel framework to enhance prediction using information about the mobility habits of existing users. At the core of the framework is a hierarchical Bayesian model, a type of probabilistic semantic analysis [7], representing the intuition that the temporal features of the new user’s location habits are likely to be similar to those of an existing user in the system. We evaluate this framework on the real life location habits of 38 users in the Nokia Lausanne dataset, showing that accuracy is improved by 16%, relative to the state of the art, when predicting the next location of new users

Resource-Aware Junction Trees for Efficient Multi-Agent Coordination

In this paper we address efficient decentralised coordination of cooperative multi-agent systems by taking into account the actual computation and communication capabilities of the agents. We consider coordination problems that can be framed as Distributed Constraint Optimisation Problems, and as such, are suitable to be deployed on large scale multi-agent systems such as sensor networks or multiple unmanned aerial vehicles. Specifically, we focus on techniques that exploit structural independence among agents’ actions to provide optimal solutions to the coordination problem, and, in particular, we use the Generalized Distributive Law (GDL) algorithm. In this settings, we propose a novel resource aware heuristic to build junction trees and to schedule GDL computations across the agents. Our goal is to minimise the total running time of the coordination process, rather than the theoretical complexity of the computation, by explicitly considering the computation and communication capabilities of agents. We evaluate our proposed approach against DPOP, RDPI and a centralized solver on a number of benchmark coordination problems, and show that our approach is able to provide optimal solutions for DCOPs faster than previous approaches. Specifically, in the settings considered, when resources are scarce our approach is up to three times faster than DPOP (which proved to be the best among the competitors in our settings)