In this paper, we present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.

We describe some experiments which simulate a grounded approach to the acquisition of the cognitive and linguistic competence required to communicate propositional logic sentences. This encompasses both the construction of a conceptualisation of its environment by each individual agent and of a shared language by the population. The processes of conceptualisation and language acquisition in each individual agent are based on general purpose cognitive capacities, such as categorisation, discrimination, invention, adoption and induction. The construction of a shared language by the population is achieved using a particular type of linguistic interaction, known as the evaluation game, which gives rise to a common set of linguistic conventions through a process of self-organisation. This work addresses the problem of the acquisition of both the semantics and the syntax of propositional logic. Trying to learn these two aspects at the same time is more difficult than learning the semantics or the syntax of propositional logic separately. Because the agents must coordinate their linguistic behaviour taking into account only the subset of objects which constitutes the topic of a particular linguistic interaction. This means that a pair of agents can communicate successfully about a particular subset of objects (a topic) even if they use different conceptualisations (formulas) in order to identify the same topic. And this introduces a high degree of ambiguity in the interpretation process the agents have to deal with when they try to construct a shared communication language. In spite of this, the results of the experiments show that at the end of the simulation runs the individual agents build different conceptualisations and grammars, but that the conceptualisations and grammars of the agents in the population are compatible in the sense that they guarantee the unambiguous communication of propositional logic sentences.

A true human-level learner should be able to deliberately construct its own knowledge, its processes of reasoning resulting in a new knowledge, its system of values and goals, and the scenario of its cognitive growth. These capabilities require a cognitive architecture of a new kind that supports metacognition, self-awareness and self-regulation. An example architecture design called Constructor is described in this work. The main distinguishing feature of this architecture is its virtually unlimited self-regulated cognitive growth ability. Other features include metacognition, self-awareness, and an intrinsic embodiment in virtual reality that is used, e.g., for active construction of cognitive and learning processes.

In this paper we describe our vision on how to create a system that acquires moral rules by learning affective meaning of human experiences written on the web.

Oscillatory neural networks have been an increasing focus of study over the last several years. Here we consider simple oscillatory memories for short-term retention of items occurring as temporal sequences. By incorporating decay as well as interference, we find that it is easy to match behavioral data from human subjects recalling temporal sequences under different situations by adjusting a single parameter in the model. These results suggest that simple oscillatory memories capture at least some key properties of human short-term memory, and might be used effectively in future biologically-inspired cognitive architectures.

The anthropological and economic history of humanity gives evidence of a progression of cognitive frameworks. There are three cognitive perspectives, in order: living in the present, living in the past, and living in the future. They correspond to three levels of competency with abstract thought: concrete thought only, abstract thought with correlations, and abstract thought with both correlations and causality. This appears to explain the fundamental differences between primitive cultures, traditional cultures, and modern cultures: differences in economics, politics, personality, and anthropological differences in general. So, not only does this theory succinctly explain a wide range of human behavior, but because it does, it appears to be a valid theory and a promising way to decompose abstract thought into its component parts for future cognitive research. These frameworks are discussed along with their implications of exploiting this progression to simplify the problem of developing an AI.

A deeply-interactive hybrid neural-symbolic cognitive architecture is defined as one in which the neural-net and symbolic components interact frequently and dynamically, so that each intervenes significantly in the other's internal operations, and the two form a combined dynamical system at the time-scale of each component's individual cognitive operations.  An example architecture of this nature that is currently under development is described: OpenCog NS, based on integration of the OpenCog cognitive architecture (which incorporates symbolic, evolutionary and connectionist aspects) with a hierarchical attractor neural network (HANN).  In this integrated architecture, the neural and non-neural aspects each play major roles, and the depth of the interconnection is revealed for example in the facts that symbolic reasoning intervenes in the process of attractor formation within the HANN, whereas the HANN plays a major role in guiding the individual steps of logical inference and evolutionary program learning processes.

This paper argues that supervised cognitive growth in artifacts will be very difficult to achieve without detailed knowledge about systems’ internal states. Physical information is too low level to provide a useful understanding of a system’s behavior, and it is more pragmatically useful to take a mental stance towards an artificial system and interpret its actions in terms of mental states. This mental stance is similar to Dennett’s intentional stance, except the ascription of beliefs and rationality in the intentional stance is replaced by the attribution of low level mental states in the mental stance. In some cases it might also be useful to take a conscious stance towards an artificial system that interprets its behavior as the outcome of a conscious decision making process. Since most artifacts lack language, automatic analysis techniques have to be used to identify the contents of their minds, and the second half of this paper suggests how some of the earlier work of Aleksander and Atlas can be applied in this area.

Many biological models of human motivation and behavior posit a functional division between those subsystems respon- sible for approach and avoidance behaviors. Gray and McNaughton's (2000) revised Reinforcement Sensitivity Theory (RST) casts this distinction in terms of a Behavioral Activation System (BAS) and a Fight-Flight-Freeze System (FFFS), mediated by a third, conflict resolution system — the Behavioral Inhibition System (BIS). They argued that these are fundamental, functionally distinct systems. The model has been highly influential both in personality psychology, where it provides a biologically-based explanation of traits such as extraversion and neuroticism, and in clinical psychology wherein state disorders such as Major Depressive Disorder and Generalized Anxiety Disorder can be modeled as differences in baseline sensitivities of one or more of the systems. In this paper, we present work in progress on implementing a simplified simulation of RST in a set of embodied virtual characters. We argue that RST provides an interesting and potentially powerful starting point for cognitive architectures for various applications, including interactive entertainment, in which simulation of human-like affect and personality is important.

This paper presents a system for advanced verbal interactions between humans and artificial agents with the aim to learn a simple language in which words and their meaning are grounded in sensory-motor experiences of the agent, and which allows agents to interact and cooperate with humans in shared environments. The system learns grounded language models from examples with a minimum of user intervention and without feedback, and it has been used to understand and subsequently to generate appropriate natural language descriptions of real objects and to engage in verbal interactions with a human partner.