An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Learning in evolutionary environments

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A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering