Multi-Agent Planning with Planning Graph

In this paper, we consider planning for multi-agents situations in STRIPS-like domains with planning graph. Three possible relationships between agents' goals are considered in order to evaluate plans: the agents may be collaborative, adversarial or indifferent entities. We propose algorithms to deal with each situation. The collaborative situations can be easily dealt with the original Graphplan algorithm by redefining the domain in a proper way. Forward-chaining and backward chaining algorithms are discussed to find infallible plans in adversarial situations. In case such plans cannot be found, the agent can still attempt to find a plan for achieving some part of the goals. A forward-chaining algorithm is also proposed to find plans for agents with independent goals

Improvements on a simple muscle-based 3D face for realistic facial expressions

Facial expressions play an important role in face-to-face communication. With the development of personal computers capable of rendering high quality graphics, computer facial animation has produced more and more realistic facial expressions to enrich human-computer communication. In this paper, we present a simple muscle-based 3D face model that can produce realistic facial expressions in real time. We extend Waters' (1987) muscle model to generate bulges and wrinkles and to improve the combination of multiple muscle actions. In addition, we present techniques to reduce the computation burden on the muscle mode

Building Embodied Agents That Experience and Express Emotions: A Football Supporter as an Example

agent that experiences and expresses emotions. Obie has an adaptive, quantitative and domain-independent emotion component which appraises events to trigger emotions. Obie's emotions are expressed via his utterances or his facial expressions. The expression via utterances is done by a simple mapping from emotions to text fragments. The mapping from emotions to facial expressions is done by a fuzzy rule-based system. Obie's utterances and facial expressions are presented in his 3D talking head. In the research described in this paper, Obie was implemented as a football supporter agent. We show how Obie experiences different emotions during a football match. We also indicate how Obie with different personalities experiences emotions differently

Some algorithms to solve a bi-objectives problem for team selection

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among (kh) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the "deep" and "wide" aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness

The Role of Serial NT-ProBNP Level in Prognosis and Follow-Up Treatment of Acute Heart Failure after Coronary Artery Bypass Graft Surgery

BACKGROUND: After coronary artery bypass graft (CABG) surgery, heart failure is still major problem. The valuable marker for it is needed. AIM: Evaluating the role of serial NT-proBNP level in prognosis and follow-up treatment of acute heart failure after CABG surgery. METHODS: The prospective, analytic study evaluated 107 patients undergoing CABG surgery at Ho Chi Minh Heart Institute from October 2012 to June 2014. Collecting data was done at pre- and post-operative days with measuring NT-proBNP levels on the day before operation, 2 hours after surgery, every next 24 h until the 5th day, and in case of acute heart failure occurred after surgery. RESULTS: On the first postoperative day (POD1), the NT-proBNP level demonstrated significant value for AHF with the cut-off point = 817.8 pg/mL and AUC = 0.806. On the second and third postoperative day, the AUC value of NT- was 0.753 and 0.751. It was statistically significant in acute heart failure group almost at POD 1 and POD 2 when analyzed by the doses of dobutamine, noradrenaline, and adrenaline (both low doses and normal doses). CONCLUSION: Serial measurement of NT-proBNP level provides useful prognostic and follow-up treatment information in acute heart failure after CABG surgery

Awareness and preparedness of healthcare workers against the first wave of the COVID-19 pandemic: A cross-sectional survey across 57 countries.

BACKGROUND: Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave. METHODS: This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected COVID-19 patients and prior COVID-19 case-management training. RESULTS: We surveyed 24,653 HCWs from 371 hospitals across 57 countries and received 17,302 responses from 70.2% HCWs overall. The median COVID-19 preparedness score was 11.0 (interquartile range [IQR] = 6.0-14.0) and the median awareness score was 29.6 (IQR = 26.6-32.6). HCWs at COVID-19 designated facilities with previous outbreak experience, or HCWs who were trained for dealing with the SARS-CoV-2 outbreak, had significantly higher levels of preparedness and awareness (p<0.001). Association rule mining suggests that nurses and doctors who had a 'great-extent-of-confidence' in handling suspected COVID-19 patients had participated in COVID-19 training courses. Male participants (mean difference = 0.34; 95% CI = 0.22, 0.46; p<0.001) and nurses (mean difference = 0.67; 95% CI = 0.53, 0.81; p<0.001) had higher preparedness scores compared to women participants and doctors. INTERPRETATION: There was an unsurprising high level of awareness and preparedness among HCWs who participated in COVID-19 training courses. However, disparity existed along the lines of gender and type of HCW. It is unknown whether the difference in COVID-19 preparedness that we detected early in the pandemic may have translated into disproportionate SARS-CoV-2 burden of disease by gender or HCW type

Creating emotions and facial expressions for embodied agents

In this thesis we consider embodied agents which are represented by an animated talking head. For such embodied agents to be believable, the minds of agents should not be restricted to model reasoning, intelligence and knowledge but also emotions and personality. Furthermore, it is necessary to pay attention not only to the agent's capacities for natural language interaction but also to its non-verbal aspects of expression