40 research outputs found
Fuzzy argumentation for trust
In an open Multi-Agent System, the goals of agents acting on behalf of their owners often conflict with each other. Therefore, a personal agent protecting the interest of a single user cannot always rely on them. Consequently, such a personal agent needs to be able to reason about trusting (information or services provided by) other agents. Existing algorithms that perform such reasoning mainly focus on the immediate utility of a trusting decision, but do not provide an explanation of their actions to the user. This may hinder the acceptance of agent-based technologies in sensitive applications where users need to rely on their personal agents. Against this background, we propose a new approach to trust based on argumentation that aims to expose the rationale behind such trusting decisions. Our solution features a separation of opponent modeling and decision making. It uses possibilistic logic to model behavior of opponents, and we propose an extension of the argumentation framework by Amgoud and Prade to use the fuzzy rules within these models for well-supported decisions
Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
Global optimization of decision trees has shown to be promising in terms of
accuracy, size, and consequently human comprehensibility. However, many of the
methods used rely on general-purpose solvers for which scalability remains an
issue. Dynamic programming methods have been shown to scale much better because
they exploit the tree structure by solving subtrees as independent subproblems.
However, this only works when an objective can be optimized separately for
subtrees. We explore this relationship in detail and show necessary and
sufficient conditions for such separability and generalize previous dynamic
programming approaches into a framework that can optimize any combination of
separable objectives and constraints. Experiments on five application domains
show the general applicability of this framework, while outperforming the
scalability of general-purpose solvers by a large margin
Solving Multi-agent MDPs Optimally with Conditional Return Graphs
In cooperative multi-agent sequential decision making under uncertainty, agents must coordinate in order find an optimal joint policy that maximises joint value. Typical solution al- gorithms exploit additive structure in the value function, but in the fully-observable multi-agent MDP setting (MMDP) such structure is not present. We propose a new optimal solver for so-called TI-MMDPs, where agents can only af- fect their local state, while their value may depend on the state of others. We decompose the returns into local returns per agent that we represent compactly in a conditional re- turn graph (CRG). Using CRGs the value of a joint policy as well as bounds on the value of partially specified joint policies can be efficiently computed. We propose CoRe, a novel branch-and-bound policy search algorithm building on CRGs. CoRe typically requires less runtime than the avail- able alternatives and is able to find solutions to problems previously considered unsolvable