Ants don't have friends: thoughts on socially intelligent agents

The question what is an agent? has been under dis cussion for many years. However, a consensus exists that the term 'agent' only makes sense in a multi-agent context - namely if there are at least two agents and assuming interaction and or communication between the agents. Agent research is generally done fairly independently in dfferent research areas, separated by the nature of the agents - natural or articial. This paper presents some thoughts on agency and sociality. Social intelligence is studied in the context of human style forms of social behaviour. Issues like embodiment, believability, rationality, social understanding,and different levels of social organisation and control are discussed

Semantics and Conversations for an Agent Communication Language

We address the issues of semantics and conversations for agent communication languages and the Knowledge Query Manipulation Language (KQML) in particular. Based on ideas from speech act theory, we present a semantic description for KQML that associates ``cognitive'' states of the agent with the use of the language's primitives (performatives). We have used this approach to describe the semantics for the whole set of reserved KQML performatives. Building on the semantics, we devise the conversation policies, i.e., a formal description of how KQML performatives may be combined into KQML exchanges (conversations), using a Definite Clause Grammar. Our research offers methods for a speech act theory-based semantic description of a language of communication acts and for the specification of the protocols associated with these acts. Languages of communication acts address the issue of communication among software applications at a level of abstraction that is useful to the emerging software agents paradigm.Comment: Also in in "Readings in Agents", Michael Huhns and Munindar Singh (eds), Morgan Kaufmann Publishers, In

Performance testing of distributed computational resources in the software development phase

A grid software harmonization is possible through adoption of standards i.e. common protocols and interfaces. In the development phase of standard implementation, the performance testing of grid subsystems can detect hidden software issues which are not detectable using other testing procedures. A simple software solution was proposed which consists of a communication layer, resource consumption agents hosted in computational resources (clients or servers), a database of the performance results and a web interface to visualize the results. Communication between agents, monitoring the resources and main control Python script (supervisor) is possible through the communication layer based on the secure XML-RPC protocol. The resource monitoring agent is a key element of performance testing which provides information about all monitored processes including their child processes. The agent is a simple Python script based on the Python psutil library. The second agent, provided after the resource monitored phase, records data from the resources in the central MySQL database. The results can be queried and visualized using a web interface. The database and data visualization scripts could be considered for a service thus the testers do not need install them to run own tests

Empirical-Rational Semantics of Agent Communication

The missing of an appropriate semantics of agent communication languages is one of the most challenging issues of contemporary AI. Although several approaches to this problem exist, none of them is really suitable for dealing with agent autonomy, which is a decisive property of artificial agents. This paper introduces an observation-based approach to the semantics of agent communication, which combines benefits of the two most influential traditional approaches to agent communication semantics, namely the mentalistic (agent-centric) and the objectivist (i.e., commitment- or protocol-oriented) approach. Our approach makes use of the fact that the most general meaning of agent utterances lays in their expectable consequences in terms of agent actions, and that communications result from hidden but nevertheless rational and to some extent reliable agent intentions. In this work, we present a formal framework which enables the empirical derivation of communication meanings from the observation of rational agent utterances, and introduce thereby a probabilistic and utility-oriented perspective of social commitments

How are physical and social spaces related? – cognitive agents as the necessary “glue”

There have been very few models which explicitly include actions and effects within a physical space as well as communication and action within a social space. This paper argues that such models will be necessary if we are to understand how and why human entities organise themselves in physical space. A consequence of such models will involve a move away from relatively simple individual-based simulations towards more complex agent-based simulations due to the necessary encapsulation of the agents who act in space and communicate with peers. Thus some sort of cognitive agency will be necessary to connect the communication with the action of the individuals. This parallels Carley’s call for social network models to be agentified (Carley). Thus this paper argues that such agency will be unavoidable in adequate models of the spatial distribution of human-related actors and, further, that the spaces within which action and communication occur will have to be, at least somewhat, distinct. Thus the burdon of proof is upon those modellers who omit such aspects. To establish the potential importance of the interplay between social and physical spaces, and to illustrate the approach I am suggesting, I exhibit a couple of agent-based simulations which involve both physical and social spaces. The first of these is an abstract model whose purpose is simply to show how the topology of the social space can have a direct influence upon spatial self-organisation, and the second is a more descriptive model which aims to show how a suitable agent-based model may inform observation of social phenomena by suggesting questions and issues that need to be investigated

Inflation Targeting: to Forecast or to Simulate

Inflation targeting is a regime based to a great extent on communication and, more specifically, on using and communicating assessments of future inflation. The central banking literature, however, devotes surprisingly little attention to some important issues connected with such assessments. There are some non-trivial choices that need to be made regarding future inflation assessments on three distinct levels: construction, decision making and communication. One of the most important choices relates to the treatment of the central bank’s behaviour within the assessment. We first differentiate between two basic ways of assessing future inflation: forecast and simulation. A forecast is the most likely picture of the future. In a forecast, all agents are assumed to behave in the most likely way. A simulation, on the other hand, is the most likely picture of the future if the behaviour of one agent follows a predetermined path or is generated using a selected reaction function. The path or reaction function ascribed to the agent does not have to be the most likely one. After differentiating between a forecast and a simulation, we discuss the pros and cons of using the two ways of assessing future inflation on the three abovementioned levels.inflation targeting, forecast, simulation, central bank, decision making, communication