Jason Intentional Learning: An Operational Semantics
This paper introduces an operational semantics for defining Intentional Learning on Jason, the well known Java-based implementation of AgentSpeak(L). This semantics enables Jason to define agents capable of learning the reasons for adopting intentions based on their own experience. In this work, the use of the term Intentional Learning is strictly circumscribed to the practical rationality theory where plans are predefined and the target of the learning processes is to learn the reasons to adopt them as intentions. Top-Down Induction of Logical Decision Trees (TILDE) has proved to be a suitable mechanism for supporting learning on Jason: the first-order representation of TILDE is adequate t…
A J-MADeM Agent-Based Social Simulation to Model Urban Mobility
The mobility models followed within metropolitan areas, mainly based on the massive use of the car instead of the public transportation, will soon become unsustainable unless there is a change of citizens’ minds and transport policies. The main challenge related to urban mobility is that of getting free-flowing greener cities, which are provided with a smarter and accessible urban transport system. In this paper, we present an agent-based social simulation approach to tackle this kind of social-ecological systems. The Jason Multi-modal Agent Decision Making (JMADeM) library enable us to model and implement the social decisions made by each habitant about how to get to work every day, e.g., …
On the Macroeconomic Effect of Extortion: An Agent-Based Approach
This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the fact that there is little data on this criminal activity given its hidden nature. We develop a Bottom-up Adaptive Macroeconomics (BAM) model that simulates a healthy economy, including a moderate inflation and a reasonable unemployment rate, and test the impact of extortion on various macroeconomic signals. The BAM model defines the usual interactions among workers, firms and banks in labour, goods and credit markets. Subsequently, crime is introduced by defining the propensity of the poorest workers to become extortionists, as well as the efficiency of the police in terms of…
An Agents and Artifacts Approach to Distributed Data Mining
This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision trees built with J48, but the approach is flexible enough to allow different kinds of learning models. The twofold contribution of this work includes: i) JaCA-DDM: an extensible tool implemented in the agent oriented programming language Jason [2] and CArtAgO [10,9] to experiment DDM agent-based approaches on different, well known training sets. A…
Towards an Agent-Based Model for the Analysis of Macroeconomic Signals
This work introduces an agent-based model for the analysis of macroeconomic signals. The Bottom-up Adaptive Model (BAM) deploys a closed Walrasian economy where three types of agents (households, firms and banks) interact in three markets (goods, labor and credit) producing some signals of interest, e.g., unemployment rate, GDP, inflation, wealth distribution, etc. Agents are bounded rational, i.e., their behavior is defined in terms of simple rules finitely searching for the best salary, the best price, and the lowest interest rate in the corresponding markets, under incomplete information. The markets define fixed protocols of interaction adopted by the agents. The observed signals are em…
A Windowing strategy for Distributed Data Mining optimized through GPUs
Abstract This paper introduces an optimized Windowing based strategy for inducing decision trees in Distributed Data Mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48; finding instances not covered by this tree (counter examples) in the remaining training examples, adding them to the window to induce a new tree; and repeating until a termination criterion is met. In this way, the number of training examples required to induce the tree is reduced considerably, while maintaining the expected accuracy levels; which is paid in terms of time performance. Our proposed enhancements…