Why drug shortages are an ethical issue

Drug shortages are a growing problem in developed countries. To some extent they are the result of technical and organisational failures, but to view drug shortages simply as technical and economic phenomena is to miss the fact that they are also ethical and political issues. This observation is important because it highlights both the moral and political imperative to respond to drug shortages as vigorously as possible, and the need for those addressing shortages to do so in ethically and politically sophisticated ways. This brief article outlines the ethical issues that need to be considered by anyone attempting to understand or address drug shortages

A flexible coupling approach to multi-agent planning under incomplete information

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-012-0569-7Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, and the Valencian Prometeo project 2008/051.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). A flexible coupling approach to multi-agent planning under incomplete information. 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Integrating BDI agents with Agent-based simulation platforms

Agent-Based Models (ABMs) is increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is exible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community

Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Abstract. We describe our previous and current efforts towards achiev-ing an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration. Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this

Complexity results for probabilistic datalog±

We study the query evaluation problem in probabilistic databases in the presence of probabilistic existential rules. Our focus is on the Datalog± family of languages for which we define the probabilistic counterpart using a flexible and compact encoding of probabilities. This formalism can be viewed as a generalization of probabilistic databases, as it allows to generate new facts from the given ones, using so-called tuple-generating dependencies, or existential rules. We study the computational cost of this additional expressiveness under two different semantics. First, we use a conventional approach and assume that the probabilistic knowledge base is consistent and employ the standard possible world semantics. Thereafter, we introduce a probabilistic inconsistency-tolerant semantics, which we call inconsistency-tolerant possible world semantics. For both of these cases, we provide a thorough complexity analysis relative to different languages, drawing a complete picture of the complexity of probabilistic query answering in this family

Believable Bot Navigation via Playback of Human Traces

Abstract Imitation is a powerful and pervasive primitive underlying examples of intelligent behavior in nature. Can we use it as a tool to help build artificial agents that behave like humans do? This question is studied in the context of the BotPrize competition, a Turing-like test where computer game bots compete by attempting to fool human judges into thinking they are just another human player. One problem faced by such bots is that of human-like navigation within the virtual world. This chapter describes the Human Trace Controller, a component of the UTˆ2 bot which took second place in the BotPrize 2010 competition. The controller uses a database of recorded human games in order to quickly retrieve and play back relevant segments of human navigation behavior. Empirical evidence suggests that the method of direct imitation allows the bot to effectively solve several navigation problems while moving in a human-like fashion.

Exception diagnosis in multiagent contract executions

We propose a diagnosis procedure that agents can use to explain exceptions to contract executions. Contracts are expressed by social commitments associated with temporal constraints. The procedure reasons from the relations among such commitments, and returns one amongst different possible mismatches that may have caused an exception. In particular, we consider two possibilities: misalignment, when two agents have two different views of the same commitment, and misbehavior, when there is no misalignment, but a debtor agent fails to oblige. We also provide a realignment policy that can be applied in case of a misalignment. Our formalization uses a reactive form of Event Calculus. We illustrate the workings of our approach by discussing a delivery process from e-commerce as a case study