Luis Lamb

AAAI Conference 2010 Conference Paper

Combining Human Reasoning and Machine Computation: Towards a Memetic Network Solution to Satisfiability

• Daniel Farenzena
• Luis Lamb
• Ricardo Araújo

We propose a framework where humans and computers can collaborate seamlessly to solve problems. We do so by developing and applying a network model, namely Memenets, where human knowledge and reasoning are combined with machine computation to achieve problem-solving. The development of a Memenet is done in three steps: first, we simulate a machine-only network, as previous results have shown that memenets are efficient problem-solvers. Then, we perform an experiment with human agents organized in a online network. This allows us to investigate human behavior while solving problems in a social network and to postulate principles of agent communication in Memenets. These postulates describe an initial theory of how human-computer interaction functions inside social networks. In the third stage, postulates of step two allow one to combine human and machine computation to propose an integrated Memenet-based problemsolving computing model.

IJCAI Conference 2007 Conference Paper

• Ricardo Araujo
• Luis Lamb

Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of available resources. When requirements and constraints are dynamic, learning agents may be needed to allow for adaptation. One way of accomplishing learning is to observe past outcomes, using such information to improve future decisions. When limits in agents' memory or observation capabilities are assumed, one must decide on how large should the observation window be. We investigate how this decision influences both agents' and system's performance in the context of a special class of distributed resource allocation problems, namely dispersion games. We show by numerical experiments over a specific dispersion game (the Minority Game) that in such scenario an agent's performance is non-monotonically correlated with her memory size when all other agents are kept unchanged. We then provide an information-theoretic explanation for the observed behaviors, showing that a downward causation effect takes place.

NeurIPS Conference 2005 Conference Paper

A Connectionist Model for Constructive Modal Reasoning

• Artur Garcez
• Luis Lamb
• Dov Gabbay

We present a new connectionist model for constructive, intuitionistic modal reasoning. We use ensembles of neural networks to represent in- tuitionistic modal theories, and show that for each intuitionistic modal program there exists a corresponding neural network ensemble that com- putes the program. This provides a massively parallel model for intu- itionistic modal reasoning, and sets the scene for integrated reasoning, knowledge representation, and learning of intuitionistic theories in neural networks, since the networks in the ensemble can be trained by examples using standard neural learning algorithms.

NeurIPS Conference 2003 Conference Paper

Reasoning about Time and Knowledge in Neural Symbolic Learning Systems

• Artur Garcez
• Luis Lamb

We show that temporal logic and combinations of temporal logics and modal logics of knowledge can be effectively represented in ar(cid: 173) tificial neural networks. We present a Translation Algorithm from temporal rules to neural networks, and show that the networks compute a fixed-point semantics of the rules. We also apply the translation to the muddy children puzzle, which has been used as a testbed for distributed multi-agent systems. We provide a complete solution to the puzzle with the use of simple neural networks, capa(cid: 173) ble of reasoning about time and of knowledge acquisition through inductive learning.