22 research outputs found
Application of the LSPI reinforcement learning technique to a co-located network negotiation problem
Application of the LSPI reinforcement learning technique to a co-located network negotiation problem
Experimental validation of a reinforcement learning based approach for a service-wise optimisation of heterogeneous wireless sensor networks
Due to their constrained nature, wireless sensor networks (WSNs) are often optimised for a specific application domain, for example by designing a custom medium access control protocol. However, when several WSNs are located in close proximity to one another, the performance of the individual networks can be negatively affected as a result of unexpected protocol interactions. The performance impact of this 'protocol interference' depends on the exact set of protocols and (network) services used. This paper therefore proposes an optimisation approach that uses self-learning techniques to automatically learn the optimal combination of services and/or protocols in each individual network. We introduce tools capable of discovering this optimal set of services and protocols for any given set of co-located heterogeneous sensor networks. These tools eliminate the need for manual reconfiguration while only requiring minimal a priori knowledge about the network. A continuous re-evaluation of the decision process provides resilience to volatile networking conditions in case of highly dynamic environments. The methodology is experimentally evaluated in a large scale testbed using both single- and multihop scenarios, showing a clear decrease in end-to-end delay and an increase in reliability of almost 25 %
Elaboration of Cognitive Decision Making Methods in the Context of Symbiotic Networking
Abstract-Recently, the concept of 'cognitive networking' has been introduced, in which reconfigurable radio networks rely on self-awareness and artificial intelligence to optimize their network performance. These cognitive networks are able to perceive current network conditions and then plan, learn and act according to end-to-end goals. This paper elaborates on different methods (network solutions) that can be used by cognitive networks for deciding on how to optimize the performance of a large number of co-located devices with different characteristics and network requirements. To this end, a negotiation based networking methodology ('symbiotic networking') is used that supports efficient network cooperation between heterogeneous devices in order to optimize their network performance. In this paper, the advantages and disadvantages of different reasoning techniques that can be used during the decision making phase are discussed
Reinforcement learning for trading dialogue agents in non-cooperative negotiations
Recent advances in automating Dialogue Management have been mainly made in
cooperative environments -where the dialogue system tries to help a human to meet
their goals. In non-cooperative environments though, such as competitive trading,
there is still much work to be done. The complexity of such an environment rises
as there is usually imperfect information about the interlocutors’ goals and states.
The thesis shows that non-cooperative dialogue agents are capable of learning how
to successfully negotiate in a variety of trading-game settings, using Reinforcement
Learning, and results are presented from testing the trained dialogue policies with
humans. The agents learned when and how to manipulate using dialogue, how to
judge the decisions of their rivals, how much information they should expose, as well
as how to effectively map the adversarial needs in order to predict and exploit their
actions. Initially the environment was a two-player trading game (“Taikun”). The
agent learned how to use explicit linguistic manipulation, even with risks of exposure
(detection) where severe penalties apply. A more complex opponent model for
adversaries was also implemented, where we modelled all trading dialogue moves as
implicitly manipulating the adversary’s opponent model, and we worked in a more
complex game (“Catan”). In that multi-agent environment we show that agents
can learn to be legitimately persuasive or deceitful. Agents which learned how to
manipulate opponents using dialogue are more successful than ones which do not
manipulate. We also demonstrate that trading dialogues are more successful when
the learning agent builds an estimate of the adversarial hidden goals and preferences.
Furthermore the thesis shows that policies trained in bilateral negotiations can be
very effective in multilateral ones (i.e. the 4-player version of Catan). The findings
suggest that it is possible to train non-cooperative dialogue agents which successfully
trade using linguistic manipulation. Such non-cooperative agents may have
important future applications, such as on automated debating, police investigation,
games, and education