Using Data Envelope Analysis to Examine US State Health Efficiencies over 2008-2015

Health spending in the United States US has been steadily rising over the past several decades The Affordable Care Act ACA became law in 2010 but was not operational until 2014 The principal intention of the legislation was to provide insurance coverage to millions of US citizens who previously did not possess health insurance to improve Americans health In our study we compare the efficiency of health care resources on a state-by-state population basis in the US between the years of 2008-2015 Efficiencies are calculated using Data Envelopment Analysis DEA DEA can be defined as a non-parametric technique that uses linear programming lp to compare the relative efficiencies of homogenous Decision Making Units DMU in transforming inputs into outputs In this case the DMUs represent the states DEA uses lp models to build an efficiency frontier The efficiency frontier is determined by the most efficient states i e DMUs Therefore the efficiency of each state can be compared against the frontier and therefore against the most efficient one

Revising Incompletely Specified Convex Probabilistic Belief Bases

We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent's beliefs are represented by a set of probabilistic formulae -- a belief base. The method involves determining a representative set of 'boundary' probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results

On Revision of Partially Specified Convex Probabilistic Belief Bases

We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves de- termining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these proba- bility distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expres- sivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revi- sion employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary dis- tribution method and the optimum entropy methods are reasonable, yet yield different results

Hybrid POMDP-BDI: An Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels

Partially observable Markov decision processes (POMDPs) and the belief-desire-intention (BDI) framework have several complimentary strengths. We propose an agent architecture which combines these two powerful approaches to capitalize on their strengths. Our architecture introduces the notion of intensity of the desire for a goal’s achievement. We also define an update rule for goals’ desire levels. When to select a new goal to focus on is also defined. To verify that the proposed architecture works, experiments were run with an agent based on the architecture, in a domain where multiple goals must continually be achieved. The results show that (i) while the agent is pursuing goals, it can concurrently perform rewarding actions not directly related to its goals, (ii) the trade-off between goals and preferences can be set effectively and (iii) goals and preferences can be satisfied even while dealing with stochastic actions and perceptions. We believe that the proposed architecture furthers the theory of high-level autonomous agent reasoning

A New Approach to Probabilistic Belief Change

One way for an agent to deal with uncertainty about its beliefs is to maintain a probability distribution over the worlds it believes are possible. A belief change operation may recommend some previously believed worlds to become impossible and some previously disbelieved worlds to become possible. This work investigates how to redistribute probabilities due to worlds being added to and removed from an agent’s belief-state. Two related approaches are proposed and analyzed

CREATE Research Symposium 2022 Book of Abstracts

This is the book of Abstracts for a recent symposium held by the CREATE research Group in TU Dublin

Maximizing Expected Impact in an Agent Reputation Network

Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observ- able Markov decision process (POMDP) do not take the benevolence of other agents for granted. We propose a new POMDP-based framework which is general enough for the specification of a variety of stochastic MAS domains involving the impact of agents on each other’s reputa- tions. A unique feature of this framework is that actions are specified as either undirected (regular) or directed (towards a particular agent), and a new directed transition function is provided for modeling the effects of reputation in interactions. Assuming that an agent must maintain a good enough reputation to survive in the network, a planning algorithm is developed for an agent to select optimal actions in stochastic MASs. Preliminary evaluation is provided via an example specification and by determining the algorithm’s complexity

A Modal Logic for the Decision-Theoretic Projection Problem

We present a decidable logic in which queries can be posed about (i) the degree of belief in a propositional sentence after an arbitrary finite number of actions and observations and (ii) the utility of a finite sequence of actions after a number of actions and observations. Another contribution of this work is that a POMDP model specification is allowed to be partial or incomplete with no restriction on the lack of information specified for the model. The model may even contain information about non-initial beliefs. Essentially, entailment of arbitrary queries (expressible in the language) can be answered. A sound, complete and terminating decision procedure is provided

A Stochastic Belief Management Architecture for Agent Control

We propose an architecture for agent control, where the agent stores its beliefs and environ- ment models as logical sentences. Given succes- sive observations, the agent’s current state (of be- liefs) is maintained by a combination of proba- bility, POMDP and belief change theory. Two ex- isting logics are employed for knowledge repre- sentation and reasoning: the stochastic decision logic of Rens et al. (2015) and p-logic of Zhuang et al. (2017) (a restricted version of a logic de- signed by Fagin et al. (1990)). The proposed ar- chitecture assumes two streams of observations: active, which correspond to agent intentions and passive, which is received without the agent’s di- rect involvement. Stochastic uncertainty, and ig- norance due to lack of information are both dealt with in the architecture. Planning, and learning of environment models are assumed present but are not covered in this proposal