Well File:

Jennings, Nicholas R

Efficient Algorithms with Performance Guarantees for the Stochastic Multiple-Choice Knapsack Problem

AAAI Conferences

We study the stochastic multiple-choice knapsack problem, where a set of Kitems, whose value and weight are random variables, arrive to the system at each time step, and a decision maker has to choose at most one item to put into the knapsack without exceeding its capacity. The goal is the decision-maker is to maximise the total expected value of chosen items with respect to the knapsack capacity and a finite time horizon.We provide the first comprehensive theoretical analysis of the problem. In particular, we propose OPT-S-MCKP, the first algorithm that achieves optimality when the value-weight distributions are known. This algorithm also enjoys O(sqrt{T}) performance loss, where T is the finite time horizon, in the unknown value-weight distributions scenario.We also further develop two novel approximation methods, FR-S-MCKP and G-S-MCKP, and we prove that FR-S-MCKP achieves O(sqrt{T}) performance loss in both known and unknown value-weight distributions cases, while enjoying polynomial computational complexity per time step.On the other hand, G-S-MCKP does not have theoretical guarantees, but it still provides good performance in practice with linear running time.

Knapsack Based Optimal Policies for Budget–Limited Multi–Armed Bandits

AAAI Conferences

In budget–limited multi–armed bandit (MAB) problems, thelearner’s actions are costly and constrained by a fixed budget.Consequently, an optimal exploitation policy may not be topull the optimal arm repeatedly, as is the case in other variantsof MAB, but rather to pull the sequence of different arms thatmaximises the agent’s total reward within the budget. Thisdifference from existing MABs means that new approachesto maximising the total reward are required. Given this, wedevelop two pulling policies, namely: (i) KUBE; and (ii)fractional KUBE. Whereas the former provides better performanceup to 40% in our experimental settings, the latteris computationally less expensive. We also prove logarithmicupper bounds for the regret of both policies, and show thatthese bounds are asymptotically optimal (i.e. they only differfrom the best possible regret by a constant factor).

An On-Line Algorithm for Semantic Forgetting

AAAI Conferences

In AI, this area Ontologies that evolve through use to support new has been studied under a variety of names such as forgetting domain tasks can grow extremely large. Moreover, and variable elimination [Eiter et al., 2006; Wang et al., large ontologies require more resources to use and 2008]. We provide a general approach for ranking knowledge have slower response times than small ones. To according to its use and cost, which can be applied to systems help address this problem, we present an online semantic that are limited by memory resources to evaluate memory forgetting algorithm that removes ontology allocation. We also provide a specific approach to select fragments containing infrequently used or cheap to which concepts to remove from an ontology, using the ranking.

Minimum Search To Establish Worst-Case Guarantees in Coalition Structure Generation

AAAI Conferences

In this context, while it methods (see, e.g., [Shehory and Kraus, 1998; Sandholm et is desirable to generate a coalition structure that al., 1999; Sen and Dutta, 2000; Dang and Jennings, 2004; maximizes the sum of the values of the coalitions, Rahwan et al., 2009b]). In this context, an important line of the space of possible solutions is often too large research is the development of anytime CSG algorithms. In to allow exhaustive search. Thus, a fundamental particular, an algorithm is "anytime" if it can return a solution open question in this area is the following: Can we at any point of time during its execution, and the quality of its search through only a subset of coalition structures, solution improves monotonically until termination. This is and be guaranteed to find a solution that is within particularly desirable in the multi-agent system context since a desirable bound β from optimum? If so, what is the agents might not always have sufficient time to run the the minimum such subset?