Cognitive Agents

Students learn through a series of systemic and innovative learning activities instead of only learning resources. However, most e-Learning systems nowadays are resources-oriented instead of processoriented. In addition, existing systems are designed without adequate pedagogical principles and provide little capabilities to cater for different student's individual needs. This paper investigates the possibility of applying process-oriented approach to design, enact, control, and refine the e-Learning processes. Furthermore, agent technology is applied to deal with dynamic situations. A processoriented and agent-based e-Learning environment is developed, with case scenario to demonstrate the usability of the approach.

This article shows progress of brain signals interpretation which identifies some of the primary emotions; those primary emotions are learned by the cognitive Agent. As a result we expect collaborative work between two or more robots that have empathy with different emotions. As part of this article development, we present the following case study. Given that human beings feel and perceive all primary emotions, we are considering doing an experiment with 6 people forming 2 groups, group A will be made up of 3 women and group B will consist of 3 men, it is worth mentioning that the ages are between 20-24 years, and that such groups do not have mental problems. These groups were shown a set of images that express the primary emotions. For Group A: Anger, Surprise, Joy and Disgust for group B: Anger, Fear and Sadness.

In this paper we propose the CTS (Concious Tutoring System) technology, a biologically plausible cognitive agent based on human brain functions.This agent is capable of learning and remembering events and any related information such as corresponding procedures, stimuli and their emotional valences. Our proposed episodic memory and episodic learning mechanism are closer to the current multiple-trace theory in neuroscience, because they are inspired by it [5] contrary to other mechanisms that are incorporated in cognitive agents. This is because in our model emotions play a role in the encoding and remembering of events. This allows the agent to improve its behavior by remembering previously selected behaviors which are influenced by its emotional mechanism. Moreover, the architecture incorporates a realistic memory consolidation process based on a data mining algorithm.

There are two main schools in knowledge representation: Connectionism and Logicism. As two independent formalisms, they are often put separately in knowledge representation. While Logicism expresses in symbolic formulas, Connectionism expresses in graphs or networks. The purpose of this paper is to investigate that it is possible we can merge the two formalisms, Connectionism and Logicism, into one formalism? Firstly, in this paper I will introduce Logicism representations. Examples are proposition logic, first order predicate logic, second order predicate logic. Next, I will introduce Connectionism. Examples of Connectionism are directed graph, semantic network, artificial neural network. And lastly, I will try to merge the two formalisms into one formalism according to three mathematical theorems.

This paper describes a new generic architecture for dialog systems enabling communication between a human user and a personal assistant based on speech acts. Dialog systems are often domain-related applications. That is, the system is developed for specific applications and cannot be reused in other domains. A major problem concerns the development of scalable dialog systems capable to be extended with new tasks without much effort. In this paper we discuss a generic dialog architecture for a personal assistant. The assistant uses explicit task representation and knowledge to achieve an "intelligent" dialog. The independence of the dialog architecture from knowledge and from tasks allows the agent to be extended without needing to modify the dialog structure. The system has been implemented in a collaborative environment in order to personalize services and to facilitate the interaction with collaborative applications like e-mail clients, document managers or design tools.

As personal assistant software matures and assumes more autonomous control of its users' activities, it becomes more critical that this software can explain its task processing. It must be able to tell the user why it is doing what it is doing, and instill trust in the user that its task knowledge reflects standard practice and is being appropriately applied. We will describe the ICEE (Integrated Cognitive Explanation Environment) explanation system and its approach to explaining task reasoning. Key features include (1) an architecture ...

Increasingly, organizations are geographically distributed with activities coordinated and integrated through the use of information technology. Such organizations face constant change and the corresponding need for continual learning and renewal of their workers. In this paper we describe a system called PHelpS (Peer Help System) that facilitates workers in carrying out such ìlife long learningî. When a worker runs into difficulty in carrying out a task, PHelpS provides a list of other workers who are ready, willing and able to help him or her. The worker then selects a particular helper with PHelpS supporting the subsequent help interaction. The PHelpS system acts as a facilitator to stimulate learning and collaboration, rather than as a directive agent imposing its perspectives on the workers.

A basic claim of this paper is that the foundational theoretical problem of the social sciences -the possibility of unconscious, unplanned forms of cooperation and intelligence among intentional agents (the very hard issue of the 'invisible hand', of the 'spontaneous social order' but also of 'social functions') -will eventually be clarified thanks to the contribution of AI (and, in particular, of cognitive Agent modelling, learning, and MAS) and its entering the social simulation domain. After introducing Multi-Agent-Based Social Simulation and its trends, the limits of the very popular notion of 'emergence' are discussed, Smith's and Hayek's view of 'spontaneous social order' are critically introduced, and serious contradictions in the theory of 'social functions' among intentional agents are pointed out. The problem is how to reconcile the 'external' teleology that orients the agent's behaviour with the 'internal' teleology governing it. In order to account for the functional character of intentional action, we need a somewhat sophisticated model of intention, and a different view of layered cognitive architectures combining explicit beliefs and goals with association and conditioning. On such a basis we sketch a model of unknown functions impinging on intentional actions through a high level form of (MA) reinforcement learning. This model accounts for both eu-functions and dys-functions, autonomous and heteronomous functions. It is argued that, in order to reproduce some behaviour, its effects should not necessarily be 'good', i.e. useful for the goal of the agent or of some higher macro-system.

In a multi-agent system, agents are carrying out certain tasks by executing plans. Consequently, the problem of finding a plan, given a certain goal, has been given a lot of attention in the literature. Instead of concentrating on this problem, the focus of this paper is on cooperation between agents which already have constructed plans for their goals. By cooperating, agents might reduce the number of actions they have to perform in order to fulfill their goals. The key idea is that in carrying out a plan an agent possibly produces side products that can be used as resources by other agents. As a result, an other agent can discard some of its planned actions. This process of exchanging products, called plan merging, results in distributed plans in which agents become dependent on each other, but are able to attain their goals more efficiently. In order to model this kind of cooperation, a new formalism is developed in which side products are modeled explicitly. The formalism is a resource logic based on the notions of resource, skill, goal, and service. Starting with some resources, an agent can perform a number of skills in order to produce other resources which suffice to achieve some given goals. Here, a skill is an elementary production process taking as inputs resources satisfying certain constraints. A service is a serial or parallel composition of skills acting as a program. An operational semantics is developed for these services as programs. Using this formalism, an algorithm for plan merging is developed, which is anytime and runs in polynomial time. Furthermore, a variant of this algorithm is proposed that handles the exchange of resources in a more flexible way. The ideas in the paper will be illustrated by an example from public transportation.

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This paper is based on the tutorial given as part of the tutorial programme of CLIMA-VI. The tutorial aimed at giving an overview of the various features available in Jason, a multi-agent systems development platform that is based on an interpreter for an extended version of AgentSpeak. The BDI architecture is the best known and most studied architecture for cognitive agents, and AgentSpeak is an elegant, logic-based programming language inspired by the BDI architecture.

Classical computing has well-established formalisms for specifying, refining, composing, proving, and otherwise reasoning about computations. These formalisms have matured over the past 70 years or so. Unconventional Computing includes the use novel kinds of substrates -from black holes and quantum effects, through to chemicals, biomolecules, even slime moulds -to perform computations that do not conform to the classical model. Although many of these substrates can be "tortured" to perform classical computation, this is not how they "naturally" compute. Our ability to exploit unconventional computing is partly hampered by a lack of corresponding programming formalisms: we need models for building, composing, and reasoning about programs that execute in these substrates. What might, say, a slime mould programming language look like? Here I outline some of the issues and properties of these unconventional substrates that need to be addressed to find "natural" approaches to programming them. Important concepts include embodied real values, processes and dynamical systems, generative systems and their meta-dynamics, and embodied self-reference.

While there is much work in the literature on agent component technologies, there are comparatively few results on the engineering issues arising from the integration of these technologies into a complete and coherent agent architecture. This paper addresses these issues from both implementation and general architectural design point of view. The KGP (Knowledge, Goals and Plan) agent model developed in the EU SOCS project (IST-2001 32530) is used as an example to illustrate these engineering issues.

Eunoia is a cognitive neural architecture designed to move closer to conscious artificial general intelligence (AGI) by integrating principles from mathematics, psychology, and neuroscience. This paper presents a comprehensive formalization of Eunoia's architecture, emphasizing its core cognitive modules, emotional mapping system, self-adaptive behavior models, and neural network design. We detail the mathematical underpinnings of Eunoia's learning algorithms and knowledge representation, the algorithmic strategies enabling self-monitoring and dynamic adaptation, and the behavioral mapping techniques that imbue the system with emotional cognition capabilities. The Eunoia cognitive architecture is structurally inspired by the human brain-it includes perception processing, memory (short-term and long-term), an analytical reasoning unit, an emotional state module, and a decision-making center, all coordinated by a self-monitoring mechanism akin to metacognitive awareness. We also describe neurological enhancements implemented in Eunoia, such as brain-inspired parallel processing and plasticity for continual learning. Experimental results from prototype implementations demonstrate Eunoia's ability to learn from complex stimuli, exhibit contextdependent emotional responses, and adapt its behavior over time, showcasing a tangible step toward conscious AGI. In conclusion, we discuss how Eunoia's multidisciplinary integration of mathematical models, behavioral science, and neural mapping represents a novel approach to emulating machine consciousness, and we outline future work needed to advance its capabilities. 1

Eunoia is a cognitive neural architecture designed to move closer to conscious artificial general intelligence (AGI) by integrating principles from mathematics, psychology, and neuroscience. This paper presents a comprehensive formalization of Eunoia's architecture, emphasizing its core cognitive modules, emotional mapping system, self-adaptive behavior models, and neural network design. We detail the mathematical underpinnings of Eunoia's learning algorithms and knowledge representation, the algorithmic strategies enabling self-monitoring and dynamic adaptation, and the behavioral mapping techniques that imbue the system with emotional cognition capabilities. The Eunoia cognitive architecture is structurally inspired by the human brain-it includes perception processing, memory (short-term and long-term), an analytical reasoning unit, an emotional state module, and a decision-making center, all coordinated by a self-monitoring mechanism akin to metacognitive awareness. We also describe neurological enhancements implemented in Eunoia, such as brain-inspired parallel processing and plasticity for continual learning. Experimental results from prototype implementations demonstrate Eunoia's ability to learn from complex stimuli, exhibit contextdependent emotional responses, and adapt its behavior over time, showcasing a tangible step toward conscious AGI. In conclusion, we discuss how Eunoia's multidisciplinary integration of mathematical models, behavioral science, and neural mapping represents a novel approach to emulating machine consciousness, and we outline future work needed to advance its capabilities. 1

Eunoia is a cognitive neural architecture designed to move closer to conscious artificial general intelligence (AGI) by integrating principles from mathematics, psychology, and neuroscience. This paper presents a comprehensive formalization of Eunoia's architecture, emphasizing its core cognitive modules, emotional mapping system, self-adaptive behavior models, and neural network design. We detail the mathematical underpinnings of Eunoia's learning algorithms and knowledge representation, the algorithmic strategies enabling self-monitoring and dynamic adaptation, and the behavioral mapping techniques that imbue the system with emotional cognition capabilities. The Eunoia cognitive architecture is structurally inspired by the human brain -it includes perception processing, memory (short-term and long-term), an analytical reasoning unit, an emotional state module, and a decision-making center, all coordinated by a self-monitoring mechanism akin to metacognitive awareness. We also describe neurological enhancements implemented in Eunoia, such as brain-inspired parallel processing and plasticity for continual learning. Experimental results from prototype implementations demonstrate Eunoia's ability to learn from complex stimuli, exhibit contextdependent emotional responses, and adapt its behavior over time, showcasing a tangible step toward conscious AGI. In conclusion, we discuss how Eunoia's multi-disciplinary integration of mathematical models, behavioral science, and neural mapping represents a novel approach to emulating machine consciousness, and we outline future work needed to advance its capabilities. 1

Abstract: this document is to describe the FevahrCassie cognitive agent, and to provide instructionsfor building SNePS/GLAIR cognitive agents based on FevahrCassie in the ASCII world.Throughout this document "entity" will mean a mental entity denoted by a SNePS term (node), and"object" will mean an object in the real or simulated world in which the agent operates.2 The Fevahr EnvironmentThe Fevahr operates in a 17 #17 # room containing:

Using Anti-air warfare (AAW) tactics and concepts of operations, this research explores the effective factors of convoy operations. A discrete event simulation (DES) was built by modeling ships, and their sensors and weapons. The model was used to simulate over 1.5 million naval battles varying 99 input variables using systematically created data combinations. To select the input settings over a specific range of input variables, a nearly orthogonal nearly balanced (NOB) Latin hypercube design was used. The effects of these input changes on the outputs were analyzed. The response variable was the survival of the High Value Unit (HVU) as a binary outcome. According to the analysis, in a convoy operation under air threat, the surface-to-air missile (SAM) specifications of the screen ships, the staying power of the HVU, and the anti-ship missile (ASM) specifications of the enemy ships had the most significant effect on the survival of the HVU.

This work proposes an analysis of the cognitive and motivational components of hope, its basic properties, and the affective dispositions and behaviors it is likely to induce. In our view current treatments of hope do not fully account for its specificity, by making hope overlap with positive expectation or some specification of positive expectation. In contrast, we attempt to highlight the distinctive features of hope, pointing to its differences from positive expectation, as well as from a sense of successful agency, optimism, trust, and faith. Fear and anxiety are also addressed, and the latter is analyzed as a state of mind implying both fear and hope. Finally, the relationship between hope and motivation is explored, and "active" hope is compared with "passive" hope. Some concluding remarks summarize the results of our analysis, and stress the role of hope in fostering the individual's well-being.

This paper presents a cognitive agent model capable of showing situations where self-generated actions are attributed to other agents, as, for example, for patients suffering from schizophrenia. The mechanism underlying the model involves inverse mirroring: mapping preparation states onto sensory representations of observed actions. It is shown how this mechanism can develop based on Hebbian learning. The model provides a basis for applications to human-like virtual agents in the context of for example, training of therapists or agent-based generation of virtual stories.

Using so-called puzzles of reference as a foil, I very briefly analyze the semantic tradition that generates them – the New Theory of Reference augmented by Direct Reference Theory (NTR+DRT) – and present an unorthodox version of a Sellarsian pragmatist alternative inspired by (and significantly altered and simplified from) suggestive work by Michael Williams (2013, 2015). It’s not that I believe this alternative is correct to the letter so much as cleanly accounts for our actual abilities to smoothly handle the puzzles of reference “vexing” the foundations of analytic philosophy of language in the NTR+DRT tradition. First, I sketch a basic use-conditional alternative leveraging the analytical machinery and more explicit engagement with the world familiar from the NTR+DRT tradition while still embracing the structure and powerful insights of inferentialist accounts. I attempt to show that we don’t have to fully abandon the mechanics and broad approach familiar from the NTR+DRT tradition to get some of the value of this liberally interpreted pragmatist take on meaningfulness. Finally, I wrap up with a suggestion that the NTR+DRT approach is very much the product of its historical development and that we should be led by the linguistic phenomena rather than theoretical pre-commitment.

This article shows progress of brain signals interpretation which identifies some of the primary emotions; those primary emotions are learned by the cognitive Agent. As a result we expect collaborative work between two or more robots that have empathy with different emotions. As part of this article development, we present the following case study. Given that human beings feel and perceive all primary emotions, we are considering doing an experiment with 6 people forming 2 groups, group A will be made up of 3 women and group B will consist of 3 men, it is worth mentioning that the ages are between 20-24 years, and that such groups do not have mental problems. These groups were shown a set of images that express the primary emotions. For Group A: Anger, Surprise, Joy and Disgust for group B: Anger, Fear and Sadness.

: The paper presents a preliminary result of using cognitive- and behavior-based modeling framework to simulate a network of MANETs (Mobile Adhoc NETworks) as intelligent agents in a tactical battlefield. We demonstrate the efficacies of an agent-based modeling and the need for developing formal methods for multi-agent simulations from a system of systems (SoS) perspective. The development of our model framework informs the need to describe behaviors and relationships of actors and objects in the context of a mission space, and 2) to provide a foundation for modeling agent behaviors in a way that is plausible with respect to human behavior, a specially from the standpoint of human-system interactions.

Demand is on the rise for scientifically based human-behavior models that can be quickly customized and inserted into immersive training environments to recreate a given society or culture. At the same time, there are no readily available science model-driven environments for this purpose (see survey in Sect. 2). In researching how to overcome this obstacle, we have created rich (complex) socio-cognitive agents that include a large number of social science models (cognitive, sociologic, economic, political, etc) needed to enhance the realism of immersive, artificial agent societies. We describe current efforts to apply model-driven development concepts and how to permit other models to be plugged in should a developer prefer them instead. The current, default library of behavioral models is a metamodel, or authoring language, capable of generating immersive social worlds. Section 3 explores the specific metamodels currently in this library (cognitive, sociopolitical, economic, conversational, etc.) and Sect. 4 illustrates them with an implementation that results in a virtual Afghan village as a platform-independent model. This is instantiated into a server that then works across a bridge to control the agents in an immersive, platform-specific 3D gameworld (client). Section 4 also provides examples of interacting in the resulting gameworld and some of the training a player receives. We end with lessons learned and next steps for improving both the process and the gameworld. The seeming paradox of this research is that as agent complexity increases, the easier it becomes for the agents to explain their world, their dilemmas, and their social networks to a player or trainee.

Demand is on the rise for scientifically based human-behavior models that can be quickly customized and inserted into immersive training environments to recreate a given society or culture. At the same time, there are no readily available science model-driven environments for this purpose (see survey in Sect. 2). In researching how to overcome this obstacle, we have created rich (complex) socio-cognitive agents that include a large number of social science models (cognitive, sociologic, economic, political, etc) needed to enhance the realism of immersive, artificial agent societies. We describe current efforts to apply model-driven development concepts and how to permit other models to be plugged in should a developer prefer them instead. The current, default library of behavioral models is a metamodel, or authoring language, capable of generating immersive social worlds. Section 3 explores the specific metamodels currently in this library (cognitive, socio-political, economic, conversational, etc.) and Sect. 4 illustrates them with an implementation that results in a virtual Afghan village as a platform-independent model. This is instantiated into a server that then works across a bridge to control the agents in an immersive, platform-specific 3D gameworld (client). Section 4 also provides examples of interacting in the resulting gameworld and some of the training a player receives. We end with lessons learned and next steps for improving both the process and the gameworld. The seeming paradox of this research is that as agent complexity increases, the easier it becomes for the agents to explain their world, their dilemmas, and their social networks to a player or trainee.

Categorization is one of the fundamental building blocks of cognitive systems. Object categorization has traditionally been addressed in the vision domain, even though cognitive agents are intrinsically multimodal. Indeed, biological systems combine several modalities in order to achieve robust categorization. In this paper we propose a multimodal approach to object category detection, using audio and visual information. The auditory channel is modeled on biologically motivated spectral features via a discriminative classifier. The visual channel is modeled by a state of the art part based model. Multimodality is achieved using two fusion schemes, one high level and the other low level. Experiments on six different object categories, under increasingly difficult conditions, show strengths and weaknesses of the two approaches, and clearly underline the open challenges for multimodal category detection.

Este artigo descreve as interações entre e o agente tutor e os estudantes no sistema MCOE (Multi-agent CO-operative Environment). O MCOE representa o estado da arte do projeto de pesquisa envolvendo o paradigma de agentes para modelar Sistemas Tutores Inteligentes com interface e funcionamento baseados em técnicas de programação de jogos. Os agentes reativos (componentes do cenário onde se desenvolve o jogo) e os agentes cognitivos (estudantes representados pelos seus personagens e o tutor) são modelados através de estados mentais (crenças, desejos, intenções e expectativas). O formalismo de Mora et al [MOR97; MOR98] é utilizado para representar as interações da coreografia deste estados mentais .

FLUX belongs to the high-level programming lan­ guages for cognitive agents that have been devel­ oped in recent years. Based on the established, general action representation formalism of the Flu­ ent Calculus, FLUX allows to implement complex strategies in a concise and modular fashion. In this paper, we extend the FLUX language to reason about domains involving continuous change and where actions occur concurrently. Using constraint logic programming, we show that this reasoning is performed in an efficient way.

We present a process-oriented approach to specify personalitybased behaviors in agents. The main aim is to make the agents able to reason about their choices following a preferred reasoning strategy according to a personality type. With this method, the decision-making of the agent becomes flexible enough to be reused in different scenarios while staying consistent with the used psychological theory.

As the complexity of real-world applications increases, particularly with the advent of the Internet, there is a need to incorporate organisational abstractions into computing systems to ease their design, development, and maintenance. Electronic institutions are at the heart of this approach. Electronic institutions provide a computational analogue of human organisations in which human and intelligent agents playing different organisational roles interact to accomplish individual and organisational goals. In this paper we introduce an ...

Resumo - Este artigo apresenta a definição de uma arquitetura de agentes cognitivos independentes para o pré-atendimento hospitalar. Neste contexto, a escolha de uma arquitetura distribuída e aberta baseou-se nos seguintes critérios: a) Trata-se de um problema cuja decomposição em subproblemas é natural, à medida que cada subproblema (Central Regulação, Unidade Móvel, etc) pode ser representado e implementado como um agente autônomo e independente; b) a interação entre os agentes pode tornar o sistema de pré-atendimento mais confiável, ã medida que as trocas de informações entre os agentes podem resolver situações difíceis pelo numero de especialistas e recursos exigidos de forma dinâmica (ou sobre demanda); c) o paralelismo natural dos sistema multi-agente podem contribuir de forma considerável na implementação de um sistema de alta performance. Deve-se enfatizar que tais critérios, no contexto do pré- atendimento hospitalar, facilitam a decomposição do problema em entidades fracam...

From an external perspective, cognitive agent behaviour can be described by specifying (temporal) correlations of a certain complexity between stimuli (input states) and (re)actions (output states) of the agent. From an internal perspective the agent's dynamics can be characterized by direct (causal) temporal relations between internal cognitive states of the agent. Internal dynamics and externally observable behaviour of an agent have reciprocal relations with each other. This paper contributes an approach that allows automatic generation of a behavioural specification of an agent from a cognitive process model. Furthermore, by this automated transformation, internal cognitive state properties of an agent can be related by a representation relation to externally observable behavioural patterns.

In this paper an agent-based ambient agent architecture is presented based on monitoring human's interaction with his or her environment and performing cognitive analysis of the causes of observed or predicted behaviour. Within this agent architecture, a cognitive model for the human is taken as a point of departure. From the cognitive model it is automatically derived how internal cognitive states affect human's performance aspects. Furthermore, for these cognitive states representation relations are derived from the cognitive model, expressed by temporal specifications involving events that will be monitored. The representation relations are verified on the monitoring information automatically, resulting in the identification of cognitive states, which affect the performance aspects. In such a way the ambient agent model is able to provide a more in depth cognitive analysis of causes of (un)satisfactory performance and based on this analysis to generate interventions in a knowledgeable manner. The application of the architecture proposed is demonstrated by two examples from the ambient-assisted living domain and the computer-assisted instruction domain.

Emotions are an integral part of human decision making. It follows that emotions should take part in the decision process towards the design of more realistic artificial agents. Three psychological models for emotions are examined and a corresponding algorithm is developed for each depicting its process. A generalized multi-agent model is designed to demonstrate the implementation of each of the three methods. An agent thus represents a human capable of exhibiting emotional state in response to an arbitrary emotionally charged event of varying impact.

With the rapid increase of the world's urban population, the infrastructure of the constantly expanding metropolitan areas is undergoing an immense pressure. To meet the growing demands of sustainable urban environments and improve the quality of life for citizens, municipalities will increasingly rely on novel transport solutions. In particular, Unmanned Aerial Vehicles (UAVs) are expected to have a crucial role in the future smart cities thanks to their interesting features such as autonomy, flexibility, mobility, adaptive altitude, and small dimensions. However, densely populated megalopolises of the future are administrated by several municipals, governmental and civil society actors, where vivid economic activities involving a multitude of individual stakeholders take place. In such megalopolises, the use of agents for UAVs is gaining more interest especially in complex application scenarios where coordination and cooperation are necessary. This paper sketches a visionary v...

The SNePS research group has built several imple- mentations of an embodied computational cogni- tive agent called Cassie, based on the Grounded Layered Architecture with Integrated Reasoning (GLAIR). In this document we describe a new im- plementation, in which Cassie's body and the world are simulated in Crystal Space, an environment for building 3-D games. We describe the implementa- tion

We present a sound and complete logic for automatic verification of Sim-pleAPL programs. SimpleAPL is a fragment of agent programming languages such as 3APL and 2APL designed for the implementation of cognitive agents with beliefs, goals and plans. Our logic is a variant of PDL, and allows the specification of safety and liveness properties of agent programs. We prove a correspondence between the operational semantics of SimpleAPL and the models of the logic for two example program execution strategies. We show how to translate agent programs written in SimpleAPL into expressions of the logic, and give an example in which we show how to verify correctness properties for a simple agent program using theorem-proving.

There seem to be three basic kinds of theories trying to provide explanations for the urban spatial process: the causal, the formal and the decisional. By these terms it is meant respectively theories based on cause-effect mechanisms linking the social and the spatial; theories that take some sort of spatial transformation mechanism of their own, which are triggered by the social; and finally theories that take agents' intertwining decisions as the mechanism of spatial change. Causal theories, being the oldest, are apparently the crudest, whilst the cutting edge multi-agent simulations seem more elaborate. However, the former have been able to expose basic and stable relationships between man and space, even though they have not been able to explain how individuals actually interpret and use such relationships to their own interest in different situations. The latter try to do precisely that, but very little spatial matter has as yet been included in the agents' decision-making process. Our point is that fundamental spatial attributes of the city, such as accessibility, centrality, relative position, and polarity could be included as reasoning instruments in agent-based models. In order to do that, however, cause-effect relationships should not be taken as laws or principles to be obeyed, but as means to realise profit (and utility), subject to speculation, personal interpretation and relative short sighted decisions. The paper reviews part of the configurational theory and proposes a model of production and consumption of urban space, based on agents that make decisions on a spatial basis. The model is presented in a conceptual form, and its testing in a computer simulation is also described. In order to do that, we comment on the computational approach that was used in such simulation. Some early results of our experiments with this agent-based social simulation are also mentioned.

The shadow agent is a new type of intelligent virtual agent exploiting the metaphor of the shadow as a medium. The core of the shadow agent is a behavioral architecture inspired by the BDI (Belief, Desire and Intention) cognitive agent model. The shadow agent perceives the world through two video cameras and a microphone. Advanced computer graphics techniques are used to locate the user's feet and analyses his/her behavior. The shadow agent is embodied as an animated silhouette projected on the floor using the Everywhere Display system and is endowed with simple sonic behavior. While presented as an interactive installation, this model is thought to be a generic approach that can support many more applications, including: video games, museum guides and digital performance to name a few.

The Cognitive Agent Architecture (Cougaar) is one of the most sophisticated distributed agent architectures developed today. As part of its research and evolution, Cougaar is being studied for application to large, logistics-based applications for the Department of Defense (DoD). Anticipiting future complex applications of Cougaar, we are investigating the Model Driven Architecture (MDA) approach to understand how effective it would be for increasing productivity in Cougarbased development efforts. Recognizing the sophistication of the Cougaar development environment and the limitations of transformation technologies for agents, we have systematically developed an approach that combines component assembly in the large and transformation in the small. This paper describes some of the key elements that went into the Cougaar Model Driven Architecture approach and the characteristics that drove the approach.

Abstract: This paper,presents,a multi -agent,approach,to design,of adaptive distributed collaborative and peer help environments, which addresses a number of challenges: locating appropriate human,and electronic resources depending on the help -request, motivating users to help each other, and is easily extendible "in-depth" and "in-breadth". We explore two novel approaches,in user modelling: modelling,of user social characteristics and modelling,the relationships between,users to support user,collaboration,and,peer,help.

Abstract: This paper presents a multi-agent approach to design of adaptive distributed collaborative and peer help environments, which addresses a number of challenges: locating appropriate human and electronic resources depending on the help-request, motivating users to help each other, providing design approaches that are easily extendible" in-depth" and" in-breadth". We explore the notion of modelling user social characteristics, that is a" social user model", which is as important as a cognitive user model in supporting ...

An approach to the analysis and modeling of organizational information processes of knowledge extraction and semantic communication is presented. Organizations are treated as autonomous systems situated in a certain environment. It is assumed that internal information processes of organizations that are responsible for the production of linguistic representations, can be conceptualized as internal processes of cognitive communicative agents. An original symbolic model for communicative cognitive agents is presented and an approach to map this model onto organizational processes is suggested. This model makes it possible to ground logic like communicative language in the content of organizational databases and to preserve the intentional meaning of the communication language. The original model for the so called epistemic satisfaction relations describing the phenomenon of the language grounding can be used as an analytic tool that supports the intentional modeling of organizational ...