Rational physical agent reasoning beyond logic

The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation

Embedding agents in business applications using enterprise integration patterns

This paper addresses the issue of integrating agents with a variety of external resources and services, as found in enterprise computing environments. We propose an approach for interfacing agents and existing message routing and mediation engines based on the endpoint concept from the enterprise integration patterns of Hohpe and Woolf. A design for agent endpoints is presented, and an architecture for connecting the Jason agent platform to the Apache Camel enterprise integration framework using this type of endpoint is described. The approach is illustrated by means of a business process use case, and a number of Camel routes are presented. These demonstrate the benefits of interfacing agents to external services via a specialised message routing tool that supports enterprise integration patterns

Relational Representations in Reinforcement Learning: Review and Open Problems

This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u

On the Optimized Utilization of Smart Contracts in DLTs from the Perspective of Legal Representation and Legal Reasoning

Smart contracts are computer programs stored in blockchain which open a wide range of applications but also raise some important issues. When we convert traditional legal contracts written in natural language into smart contracts written in lines of code, problems will arise. Translation errors will exist in the process of conversion since the law in natural language is ambiguous and imprecise, full of conflicts, and the emergence of new evidence may influence the processing of reasoning. This research project has three purposes: the first aims at the resolution of these problems from logic and technical perspective to develop the accuracy and human-readability of smart contracts, by exploring a more novel and advanced logic-based language to represent legal contracts, and analyzing an extended argumentation framework with rich expressiveness; the second purpose is to investigate various existing technologies like Akoma Ntoso and Legal- RuleML, making the legal knowledge and reasoning machine-readable and be linked with the real world; third, to investigate the implementation of a mature multi-agent system incorporating the software agents with sensing, inferring, learning, decision-making and social abilities that can be fitted onto DLTs

Has the Preston Curve Broken Down?

Three apparently contradictory stylized facts characterize the relationship between per capita incomes and life expectancy: (i) the existence of a strong correlation between the level of life expectancy and the level of per capita income, (ii) the absence of a significant correlation between changes in per capita income and changes in life expectancy, and (iii) the persistence of twin peaks in the distribution of life expectancy, despite their progressive disappearance from the income data. This paper seeks to reconcile these apparently contradictory findings. We argue that a data generating process in which there is a relationship between income and life expectancy for high levels of development but not for low ones can explain these stylized facts, while models that apply a uniform relationship to all countries cannot. We also argue that the slope of the relationship between income and life expectancy is significantly overestimated by standard cross-sectional estimates, with the true slope being much lower for some countries and not statistically significantly different from zero for others. Lastly, we provide evidence from an error-correction model showing that the elasticity of life expectancy to incomes has been declining both for countries at high and low levels of development. We suggest that these results can be interpreted as showing that income matters only for countries that are close enough to the world health technological frontier.Life expectancy, income growth, Preston curve, health determinants, Monte Carlo experiments.

Modeling Family Behaviors in Crowd Simulation

Modeling human behavior for a general situation is difficult, if not impossible. Crowd simulation represents one of the approaches most commonly used to model such behavior. It is mainly concerned with modeling the different human structures incorporated in a crowd. These structures could comprise individuals, groups, friends, and families. Various instances of these structures and their corresponding behaviors are modeled to predict crowd responses under certain circumstances and to subsequently improve event management, facility and emergency planning. Most currently existing modeled behaviors are concerned with depicting individuals as autonomous agents or groups of agents in certain environments. This research focuses on providing structural and state-based behavioral models for the concept of a family incorporated in the crowd. The structural model defines parents, teenagers, children, and elderly as members of the family. It also draws on the associated interrelationships and the rules that govern them. The behavioral model of the family encompasses a number of behavioral models associated with the triggering of certain well-known activities that correspond to the family’s situation. For instance, in normal cases, a family member(s) may be hungry, bored, or tired, may need a restroom, etc. In an emergency case, a family may experience the loss of a family member(s), the need to assist in safe evacuation, etc. Activities that such cases trigger include splitting, joining, carrying children, looking for family member(s), or waiting for them. The proposed family model is implemented on top of the RVO2 library that is using agent-based approach in crowd simulation. Simulation case studies are developed to answer research questions related to various family evacuation approaches in emergency situations