6,172 research outputs found
Recommended from our members
Evolving structure-function mappings in cognitive neuroscience using genetic programming
A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints, for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems.
(This article does not exactly replicate the final version published in the Journal of Swiss Psychology. It is not a copy of the original published article and is not suitable for citation.
Combating catastrophic forgetting with developmental compression
Generally intelligent agents exhibit successful behavior across problems in
several settings. Endemic in approaches to realize such intelligence in
machines is catastrophic forgetting: sequential learning corrupts knowledge
obtained earlier in the sequence, or tasks antagonistically compete for system
resources. Methods for obviating catastrophic forgetting have sought to
identify and preserve features of the system necessary to solve one problem
when learning to solve another, or to enforce modularity such that minimally
overlapping sub-functions contain task specific knowledge. While successful,
both approaches scale poorly because they require larger architectures as the
number of training instances grows, causing different parts of the system to
specialize for separate subsets of the data. Here we present a method for
addressing catastrophic forgetting called developmental compression. It
exploits the mild impacts of developmental mutations to lessen adverse changes
to previously-evolved capabilities and `compresses' specialized neural networks
into a generalized one. In the absence of domain knowledge, developmental
compression produces systems that avoid overt specialization, alleviating the
need to engineer a bespoke system for every task permutation and suggesting
better scalability than existing approaches. We validate this method on a robot
control problem and hope to extend this approach to other machine learning
domains in the future
An evolutionary behavioral model for decision making
For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud
of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud
the same evolutionary mechanism is able to assemble quite\ud
complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies
Computer modeling of human decision making
Models of human decision making are reviewed. Models which treat just the cognitive aspects of human behavior are included as well as models which include motivation. Both models which have associated computer programs, and those that do not, are considered. Since flow diagrams, that assist in constructing computer simulation of such models, were not generally available, such diagrams were constructed and are presented. The result provides a rich source of information, which can aid in construction of more realistic future simulations of human decision making
ECONOMIC AGENCY THROUGH MODULARITY THEORY
Economic agency as a matter of rational decision-making and as a problem of bounded rationality has never gone too far from its earlier formalization in the 1950s. Not that the advancement on this topic is so slow, but the same problem concerning higher level cognition as another general program of cognitive science is not as easy as behavioral studies. This paper will show a parallelism between economic agency and folkpsychological perspective, and in turn will give a short description on how folk psychology is unseparable from modularity theory. In short, then there must be a way to cope with cognition as the black box of economics if we can identify the appropriate level of description of cognitive structure, i.e.: modularity theory.bounded rationality, folk psychology, modularity theory
Semantic Mutation Testing for Multi-Agent Systems
This paper introduces semantic mutation testing (SMT) into multiagent systems. SMT is a test assessment technique that makes changes to the interpretation of a program and then examines whether a given test set has the ability to detect each change to the original interpretation. These changes represent possible misunderstandings of how the program is interpreted. SMT is also a technique for assessing the robustness of a program to semantic changes. This paper applies SMT to three rule-based agent programming languages, namely Jason, GOAL and 2APL, provides several contexts in which SMT for these languages is useful, and proposes three sets of semantic mutation operators (i.e., rules to make semantic changes) for these languages respectively, and a set of semantic mutation operator classes for rule-based agent languages. This paper then shows, through preliminary evaluation of our semantic mutation operators for Jason, that SMT has some potential to assess tests and program robustness
Agent-Based Models and Human Subject Experiments
This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms
- âŠ