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Models of Agent Cognition Assessed in Social Situations (MACASS)
1 Introduction
Composite systems are composed of parts which interact to cause the properties of the whole (what
has been called “upward causation”). Complex systems have been characterised by the additional
presence of “downward” causation [Langton], that is where the emergent properties of the system
as a whole effects the behaviour of its parts in a causal way. This makes a traditional analysis of
the whole into its parts difficult, resulting in the attribution of ‘complexity’. Thus the relation of
the behaviour of the parts to the behaviour of the whole is central to the understanding of such
complexity.
Social systems are paradigmatic examples of this sort of complexity, not only do individuals
construct their society, but, these same individuals can also be sensitive to phenomena emerging
from their collective society. Although this dual causation has been identified with respect to human
institutional processes (Giddin’s ‘structuration’), the general range and character of such emergent
behaviour has not been systematically studied. One of the reasons for this may be that such a study
bridges the fields of cognitive science and sociology, the former concentrates on individual models
of cognition which are sometime extended into composite interactive models (the “upward” causal
factors), while the latter is concerned with the characteristics of society extending downward to its
effects on the individual.
Recently the techniques social simulation have began to fill this gap. Among such simulation
techniques those of agent-based social simulation, explicitly capture the cognitive as well as social
aspects of such systems. Such simulations thus provide an experimental workbench with which to
explore parts of the envelope of possible complex behaviour in social systems .
However social simulators using such techniques face a fundamental dilemma: using more credible
models of cognition in their simulations takes large amounts of computational power, which usually
makes the extensive exploration of behaviour impractical; or using simpler cognitive models may
lead to misleading results when situated in complex social environments. Typically the social
simulator either chooses a lean computational model to animate the agents in a simulation, under
the assumption that there will be no overall loss in the richness of emergent social outcomes so that
meaningful statistical results can be considered (e.g. Carley and Svoboda, 1996); or chooses a
more elaborate cognitive model and is satisfied with examining relatively few runs (e.g. Edmonds,
1998).
This project will investigate the relationship between behaviour at the level of the individual agent
and the behaviour of their society as a whole and, in particular, how the model of individual agent
cognition impacts on the social outcomes. This will be done using a large number of social
simulations in a systematic exploration of the possibilities which will be verified by testing them
with small societies of robots and other (domain-specific) simulations in the network.
2 Key Objectives
This project aims to extend the sort of comparative studies done in a organisational setting in [4], to
a more comprehensive study using a variety of cognitive models for the agents and under a range of
social situations. It will do this both against a variety of task environments and with small
‘societies’ (8-12) of interacting robots.
In particular, this project aims to:
• evaluate and compare 3-5 different cognitive models in different 3-5 social environments. The
cognitive models will include: a BDI agent, a neural network, the ‘endorsement augmented
problem space architecture’ of Scott Moss. The Environments are to include the ‘El Farol Bar
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Problem’, Scott Moss’s co-operative action task environment, and ... ;
• gather comprehensive data about the outcomes of situating these cognitive models in these
different environments and a range of parameterisations;
• posit some guidelines on when substituting a cognitive model whose decisions may be computed
more quickly may be justified and some conditions where is it not;
• analyse the emergent social behaviours in terms of the cognitive model used, the
parameterisation of the model and the characteristics of the environment.
• make progress towards formulating a formal framework for these cognitive models and their
social outcomes;
3 Research Methods
Two main methods are proposed, both consist of the systematic exploration of outcomes via
simulation: the first method will involve a large number of purely artificial agent-based
simulations and the second a smaller number of simulations using a population of interacting
robots.
An outline of the steps are as follows (some of these occur in parallel):
1. Implement the models of cognition in SDML as modules to be plugged into agents inhabiting the
separate test environments, including: a BDI model; a connections model; problem space
architecture and endorsements; and two others taken from other network projects.
2. Implement the environments, including: 2 environments which are abstractions of the
environments from other network projects;
3. Build the robots;
4. Build an appropriate physical environment in which i) the robots can continuously recharge in
order to allow experiments over a few hours which will be necessary for the observations and
statistical evaluation of social behaviour, ii) an environment which can be matched to one of
those in one of the simulation studies
5. Build a set-up which allows to trace the trajectories of the robots (overhead camera and vision
software) and use radio link communication to record on-line data on communication and
interaction events in order to evaluate properly the behaviour of the group of robots.
6. Implement the interface from SDML to the robots, using its sockets primitives;
7. Implement one simulation environment which matches that of the robot environment as closely
as possible (the ‘shared environment’);.
8. Perform an extensive series of runs in the shared environment in simulations and with robots to
establish the relationship between the two and to inform the parameterisation of the following
runs;
9. Perform an extensive series of runs covering all the possibilities of models of cognition’s,
simulation environments and parameterisations in the computer simulations;
10.Perform an extensive series of runs covering a subset (informed by the software simulation
results) of all the possibilities of models of cognition, simulation environments and
parameterisations with the situated robots;
11.Collate, organise and analyse the data from the runs;
12. Perform any other runs suggested by the results gained so far;
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13. Publish the complete data sets from the runs on the internet together with the program code,
simulation descriptions, summaries and analyses;
14. Develop a theoretical framework for these situations, covering both simulated and robotic
experiments;
15. Relate the data and results gained with the simulations in other network projects and perform
additional simulations as appropriate;
16.Summarise and publish any methodological conclusions gained from the experience of relating
different models.
An important aspect of this project is the parallel trials of the models in both computer simulations
and with socially situated robots. It has often been found that in the transference from computer
simulation to a real environment that previous assumptions and intuitions are confounded - thus the
robot experiments are essential for ensuring that the results are valid not only in the domain of
software agents but that they apply to embodied, situated autonomous agents. Such a use of robots
to aid in the validation of social simulations is comparable to an established methodology of using
mobile robotics studies in biology [Taylor et al 1994], [Webb 1995] and cognitive science [Pfeifer
1995]. Mobile robots are also successfully used for modelling social behaviour of insect societies,
see the pioneering work done in [Deneubourg et al 1991] and [Theraulaz et al 1991]. Comparable to
the ‘animat path to Artificial Intelligence’ [Wilson 1991] which advocates to synthesize complete
artificial animals, robots can be powerful tools in social science research on ‘social agents’,
showing how models and mechanisms can perform under realistic conditions.
The major advantages of using mobile robots as ‘hardware simulations’ in order to test models of
agent cognition and behaviour is that in contrast to software simulations they are a) situated,
meaning that the robot is surrounded by the real world and not by abstractions of reality, b)
embodiment, i.e. the robot having a physical presence and spatial reality which is subject to
dynamic interactions with the environment which cannot be simulated faithfully, and c) emergence:
behaviour arises from the interaction and dynamic coupling of the robot with its environment,, it is
not a property of the agent or the environment alone ([Brooks 1991], [Arkin 1998]). However,
simulations with robots are time-consuming and limited by the nature of the robots and the
experimental set-up themselves. Reproduction of experiments and statistical evaluation in
simulation is much easier and more reliable than in real world experiments. This
simulation-versus-real-word debate (whether simulations or robots should be used for autonomous
agent research, see [Brooks and Mataric, 1994]) has meanwhile settled to a state where a
combination of both techniques is considered most valuable (for a recent example see the evolution
of a robot controller using real robots and simulations in [Wilson 1997]). Thus, in our project
computer simulations are necessary for larger scale runs over longer periods of interaction and for a
more comprehensive coverage of possible model parameterisations. Once interesting configurations
in this huge search space has been found, the robotic experiments will test the performance of these
models under real-world conditions, e.g. coping with noise, friction and changes in the environment
(like changing lighting conditions or temperature) which cannot be modeled appropriately in
simulation. The robotic experiments will therefore test a subset of the search space which is covered
by the simulations.
Examples of the cognitive models to be assessed could include: a neural network (a Jordan
recurrent network, or a Hopfield neural network), an evolutionary model of cognition based on the
Genetic Programming paradigm [Edmonds and Moss, 1997], a rule-based induction algorithm
based on an endorsement mechanism within a problem-space architecture [Scott], a BDI model
based on ‘vivid agent’, and one other taken from a collaborating project.
Examples of environment include: one where it is advantageous to cooperate; one where it is in the
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agent’s interest to discoordinate and ...
The ultimate test of the results gained is when these are used in conjunction with the results of
simulations from other projects that are verified against real-world social situations.
4 Contribution to Social Science
Although the use of agent-based simulation in social science is now established, there is very little
work on the consequence of different cognitive models on the emergent social outcomes and almost
no methodological guidance about how to combine different models or to interpret the results.
This project will provide a base for the development of systematic judgements on the
appropriateness of the different cognitive models for social simulation and give some guidance as to
the appropriate parameterisation of these models. Thus this project could aid all researchers that
use social simulation in the set-up and evaluation of their results.
In addition to the above, it will explore and develop the use of robotics in testing social simulations
as well as developing the methodology of meaningfully aligning and comparing simulations in very
different media. The lack of a systematic methodology for simulation comparison so far has
resulted in the fragmented nature of social simulation. If social simulation is to become a more
mature science then it will have to learn how to do these comparisons. This project will further this
developmental process.
5 Collaboration with other projects
Network Projects
This project addresses basic issues of simulation design, the understanding of complex social
systems and the effects of alternative models of cognition. As such it has been explicitly designed
so that it can inform almost all of the other projects in this network.
It will do this in two main ways:
 informing them of any particular consequences of having chosen one model of cognition in their
simulations as well as suggesting alternative models that may be relevant to them;
 providing a basis against which to judge the relevant parameterisation of their simulations.
In return the results of those network projects that use the same models of cognition that we do will
help us judge the significance of our results. Our results are ultimately verified through their, more
practical, simulations.
It is also expected that, as this project will be involved in more cross-collaboration with other
network projects that it will become the focus for the development of methodological guidelines for
the combination and comparison of different simulations.
The network projects which will be the most natural collaborators include:
 Information agents DBI,
 integrated climate modelling cognition,
 Eisner NNs,
 Doran BDI agents
 Gilbert-Edmund
Other Projects
Two international collaborators will be:
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 Claudio Paul-Worst at EAWAG in Zurich.
 Cristiano Castelfranchi in Italy.
6 Data
It is planned that the complete set of transcripts, statistics and analyses of the simulation runs be put
on the internet. This would enable their use by all other social simulators as benchmarks to aid
judgement of their results and as guides to the design of their models.
7 Ethical Issues
No ethical issues are anticipated to result from this project directly. Though we hope to be able to
inform and improve the design and interpretation of social simulations that do have implications in
the public domain.
8
References
Arkin, Ronald C. (1998) Behavior-based robotics. MIT Press.
Axtell, R, Axelrod, R, Epstein, J.M., and Cohen, M.D. (1996). Aligning Simulation Models: A Case Study and Results.
Computational and Mathematical Organisation Theory, 1123-141.
Bainbridge, W. S., et. al (1994). Artificial Social Intelligence, Annual Review Of Sociology, 20:407-436.
Billard, A. and Dautenhahn, K. (1998) Grounding Communication in Autonomous Robots: an Experimental Study.
Journal of Robotics and Autonomous Systems, No. 24, Vols. 1-2, pp 71-81
Brooks, R.A. (1991) Intelligence without representation. Artificial Intelligence, vol 47, pp. 139-159.
Brooks, R.A. and Mataric, M.J. (1994) Real robots, real learning problems. In: Robot learning. Eds. Connell J. H. and
Mahadevan S., Kluwer Academic Publishers, pp. 193-213
Carley K. and Newell, A. (1994). The Nature of the Social Agent. Journal of Mathematical Sociology, 19:221-262.
Carley, K. M., M. J. Prietula and Z. Lin (1998). Design Versus Cognition: The interaction of agent cognition and
organizational design on organizational performance. Journal of Artificial Societies and Social Simulation, 1(3).
Carley, K. M., Svoboda, D. M. (1996). Modeling organizational adaptation as a simulated annealing process,
Sociological Methods & Research, 1996, Vol.25, No.1, pp.138-168.
Carley, K. M. (1997). Extracting team mental models through textual analysis, Journal Of Organizational Behavior,
18:533-558.
Deneuborg, et al. (1991). The Dynamics of Collective Sorting: robot like ants and ant-like robots. In Meyer, J. A. and
Wilson S. W. From Animals to Animats, Procddings of the 1st International Conference on Simulation of Adaptive
Behaviour, 356-363.
Doran J From Computer Simulation to Artificial Societies. SCS Transactions, 14(2) 69-77.
Edmonds, B. (1998). Capturing Social Embeddedness: a constructivist approach. CPM Report 98-34, MMU,
Manchester, UK.
Edmonds, B. (1998). Modeling Socially Intelligent Agents. Applied Artificial Intelligence, 12(7).
Edmonds, B. (forthcoming). Modelling Bounded Rationality In Agent-Based Simulations using the Evolution of Mental
Models. In Brenner, T. (ed), Computational Techniques for Modelling Learning in Economics, Kluwer.
Edmonds, B. (1997). Modelling Bounded Rationality using Evolutionary Techniques. In Corne, D and Shapiro, J. (eds),
Evolutionary Computing: Selected Papers from the 1997 AISB Workshop, LNCS 1305:31-42.
Gilbert G N and Doran J Simulating Societies, UCL Press, 1994.
Gilbert G N and Conte R Artificial Societies, UCL Press, 1995.
Kennedy, J (1998). Thinking is social - Experiments with the adaptive culture model. Journal Of Conflict Resolution,
42:56-76.
Kerstin Dautenhahn (forthcoming): The Art of Designing Socially Intelligent Agents - Science, Fiction, and the Human
in the Loop. Special Issue “Socially Intelligent Agents” of Applied Artificial Intelligence.
Kerstin Dautenhahn: Grounding Agent Sociality: The Social World is its Own Best Model, From Agent Theory to
Agent Implementation (organiser Joerg Müller and Paolo Petta) symposium. In Proceedings of 14th European Meeting
on Cybernetics and Systems Research, Editor: Robert Trappl, 779-784.
Evaluating Cognitive Models In Social Situations
Page 6
Dautenhahn, K. (1999): Embodied interaction in socially intelligent life-like agents, To appear in C. L. Nehaniv (ed):
Computation for Metaphors, Analogy and Agent, Lecture Notes in Artificial Intelligence.
Moss, S., Gaylard, H., Wallis, S. and Edmonds, B. (1998). SDML: A Multi-Agent Language for Organizational
Modelling. Computational and Mathematical Organization Theory, 4, 43-69.
Moss, S., Bruce Edmonds and Steve Wallis (1997). Validation and Verification of Computational Models with Multiple
Cognitive Agents. CPM Report 97-25, MMU, Manchester, UK.
Moss, S. and Edmonds, B. (1998). Modelling Economic Learning as Modelling. Cybernetics and Systems, 29:5-37.
Moss, S. and Dautenhahn, K. (1998). Hierarchical Organisation of Robots: a Social Simulation Study. Proceedings 12th
European Simulation Multiconference ESM98, Manchester, United Kingdom June 16-19, 1998, 400-404
Pfeifer, R. (1995). Cognition - Perspectives from autonomous agents. Robotics and Autonomous Systems, 16:25-46.
Prietula M J, Carley K M, and Gasser L, Simulating Organisations, AAAI Press, 1998.
Taylor, C. and Jefferson, D. (1994). Arftificial Life as a tool for biological inquiry. Artificial Life, 1:1-13.
Theraulaz, G, Goss, S. Gerver, J. and Deneubourg, L. J. (1991). Task differentiation in politstes wasp colonies: a model
for self-organizing groups of robots. . In Meyer, J. A. and Wilson S. W. From Animals to Animats, Procddings of the
1st International Conference on Simulation of Adaptive Behaviour, 346-355.
Webb, B. (1995). Using Robots to Model Animals: a cricket test. Robotics and Autonomous Systems, 16:117-134.
Wilson, S.W., (1991) The Animat Path to AI. In From Animats to Animals: Proceedings of the First International
Conference on Simulation of Adaptive Behavior (pp 15-21), J. A. Meyer and S.W. Wilson, eds. Cambridge. Mass. The
MIT Press/Bradford Books.
Wilson, M. King, C., and Hunt, J. (1997) Evolving hierarchical robot behaviours. Robotics and Autonomous Systems.
22(3-4): 215-230.
Worden, R. P. (1996). Primate social intelligence. Cognitive Science, 20:79-616
Ye, M, Carley, K. M. (????). Radar-Soar: Towards an artificial organization composed of intelligent agents. Journal Of
Mathematical Sociology, 20:219-246.