Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.

Plan Selection for Probabilistic BDI Agents

When an agent wants to fulfill its desires about the world, the agent usually has multiple plans to choose from and these plans have different pre-conditions and additional effects in addition to achieving its goals. Therefore, for further reasoning and interaction with the world, a plan selection strategy (usually based on plan cost estimation) is mandatory for an autonomous agent. This demand becomes even more critical when uncertainty on the observation of the world is taken into account, since in this case, we consider not only the costs of different plans, but also their chances of success estimated according to the agent's beliefs. In addition, when multiple goals are considered together, different plans achieving the goals can be conflicting on their preconditions (contexts) or the required resources. Hence a plan selection strategy should be able to choose a subset of plans that fulfills the maximum number of goals while maintaining context consistency and resource-tolerance among the chosen plans. To address the above two issues, in this paper we first propose several principles that a plan selection strategy should satisfy, and then we present selection strategies that stem from the principles, depending on whether a plan cost is taken into account. In addition, we also show that our selection strategy can partially recover intention revision.

Incorporating PGMs into a BDI Architecture

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent’s beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.

Risk-aware Planning in BDI Agents

The ability of an autonomous agent to select rational actions is vital in enabling it to achieve its goals. To do so effectively in a high-stakes setting, the agent must be capable of considering the risk and potential reward of both immediate and future actions. In this paper we provide a novel method for calculating risk alongside utility in online planning algorithms. We integrate such a risk-aware planner with a BDI agent, allowing us to build agents that can set their risk aversion levels dynamically based on their changing beliefs about the environment. To guide the design of a risk-aware agent we propose a number of principles which such an agent should adhere to and show how our proposed framework satisfies these principles. Finally, we evaluate our approach and demonstrate that a dynamically risk-averse agent is capable of achieving a higher success rate than an agent that ignores risk, while obtaining a higher utility than an agent with a static risk attitude.

Double ionization of atomic negative ions in an intense laser field

In recent years there have been many studies of multiple ionization of closed shell rare gas atoms by intense laser fields. Until now no similar work has been done in the study of more diverse targets such as negative ions where low binding energies and strong electron correlations could yield distinctive behaviour. We present the first results of ionization of more than one electron from a range of atomic negative ions by intense laser pulses. Although these pulses are long by modern standards, and tend to produce sequential ionization in atoms, the positive ion yields from the negative ions do not depend predictably on the ionization potentials. This suggests that there may, intriguingly, be an alternative mechanism enhancing double ionization at low intensities.

Suppression of multiple ionization of atomic ions in intense ultrafast laser pulses

The interaction of an intense laser field with a beam of atomic ions has been investigated experimentally for the first time. The ionization dynamics of Ar+ ions and Ar neutrals in a 60 fs, 790 nm laser pulse have been compared and contrasted at intensities up to 10(16) W cm (-2). Our results show that nonsequential ionization from an Ar+ target is strongly suppressed compared with that from the corresponding neutral target. We have also observed for the first time the strong field ionization of high lying target metastable levels in the Ar+ beam.

Experimental investigation of the processes determining x-ray emission intensities from charge-exchange collisions

Absolute cross sections have been measured for single and double charge exchange and x-ray line emission for highly charged ions of C, N, 0, and Ne colliding with He, H-2 CO2, and H2O at collisions energies of 7q keV. Present results of charge exchange in He and H-2 compare favorably with previous results. For CO2 and H2O, where prior work is scarce, the classical overbarrier model is found to overestimate results by up to a factor of 3. An analysis of the relative intensities of the observed Lyman x-ray transitions indicates that capture into l states is not statistical, as collision velocities are insufficient to populate the highest angular-momentum states. The importance of autoionization following multiple capture is highlighted, and enhanced radiative stabilization following double capture is observed and compared to other studies. Present results are also discussed in terms of mechanisms likely to generate x-ray emission in comets.