Computing infinite plans for LTL goals using a classical planner

Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally extended goals, expressed in temporal logics such as LTL, to impose restrictions on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding that represents and extends the composition of the planningdomain and the B¨uchi automaton representingthe goal. This compilation scheme has been implemented and a number of experiments are reported.

Lipase Improvement: Goals and strategies

Lipases have received great attention as industrial biocatalysts in areas like oils and fats processing, detergents, baking, cheese making, surface cleaning, or fine chemistry . They can catalyse reactions of insoluble substrates at the lipid-water interface, preserving their catalytic activity in organic solvents. This makes of lipases powerful tools for catalysing not only hydrolysis, but also various reverse reactions such as esterification, transesterification, aminolysis, or thiotransesterifications in anhydrous organic solvents. Moreover, lipases catalyse reactions with high specificity, regio and enantioselectivity, becoming the most used enzymes in synthetic organic chemistry. Therefore, they display important advantages over classical catalysts, as they can catalyse reactions with reduced side products, lowered waste treatment costs, and under mild temperature and pressure conditions. Accordingly, the use of lipases holds a great promise for green and economical process chemistry.

ACACIA: an agent-based program for simulating behavior to reach long-term goals

We present ACACIA, an agent-based program implemented in Java StarLogo 2.0 that simulates a two-dimensional microworld populated by agents, obstacles and goals. Our program simulates how agents can reach long-term goals by following sensorial-motor couplings (SMCs) that control how the agents interact with their environment and other agents through a process of local categorization. Thus, while acting in accordance with this set of SMCs, the agents reach their goals through the emergence of global behaviors. This agent-based simulation program would allow us to understand some psychological processes such as planning behavior from the point of view that the complexity of these processes is the result of agent-environment interaction.

Lipase Improvement: Goals and strategies

Lipases have received great attention as industrial biocatalysts in areas like oils and fats processing, detergents, baking, cheese making, surface cleaning, or fine chemistry . They can catalyse reactions of insoluble substrates at the lipid-water interface, preserving their catalytic activity in organic solvents. This makes of lipases powerful tools for catalysing not only hydrolysis, but also various reverse reactions such as esterification, transesterification, aminolysis, or thiotransesterifications in anhydrous organic solvents. Moreover, lipases catalyse reactions with high specificity, regio and enantioselectivity, becoming the most used enzymes in synthetic organic chemistry. Therefore, they display important advantages over classical catalysts, as they can catalyse reactions with reduced side products, lowered waste treatment costs, and under mild temperature and pressure conditions. Accordingly, the use of lipases holds a great promise for green and economical process chemistry.