Planning-based techniques are powerful tools for automated narrative generation...The talk started with comments about character (agent) intention in the narratives:
Additionally, we propose a Bayesian story evaluation method to guide the planning towards believable narratives which achieve user-defined goals. Finally, we present an in- teractive user interface which enables users of our framework to modify the believability of different actions, resulting in greater narrative variety.
Riedl et al. proposed a novel approach to evaluate believability of computer generated narratives by establishing the causal relationship with actions and characters intention and perception of the story world.and comments about agents and plans in story telling:
the work of Theune et al. on Virtual Storyteller models autonomous agents and assigns them roles within the story by an external plot- agent.In order to get there we have
...promising approach to this problem is the incorporation of an intelligent Drama Manager (DM) into the simulated environment. The DM can intervene in the story as it progresses in order to (more or less gently) guide the player in an appropriate direction.DMs are taking actions in the environment, nudging players back on track (according to story line specified by an author).
We are focusing our research on one important aspect, helping the young people in improving their performance in job interviews throughtout interactions with a virtual agents acting as recruiters.Implemented on an Aria-like platform the objective was to create a program that could either challenge the user, or help create a more comfortable environment, where the user is more at ease.
Is a simulation platform for job interviews, based on the interaction of youngsters with a virtual agents acting as recruters. Those virtual agents are credible, yet tireless interlocutors. You are able to have a realistic socio-emotional interactions with them as many times as you wish. You can modulate their emotionl display and simulate a diverse range of possible interview situations.
The virtual recruiters can recognize the gestures and the tone of the voice of the user and on that base to ''decide'' autonomously which action is best- suited in each situation, without following a predefined script. This allows users to train without any social risk.
We assume that a central organizer desires to build coalition structures to carry out a given set of tasks, and that it is possible for this central or- ganizer to create new relationships between agents, although such relationship-building is assumed to incur some cost. Within this model, we investigate the problem of computing coalition struc- tures that maximize social welfare, and the problem of computing core-stable coalition structuresAn adapter should try to work towards goals like:
We provided general results, and established that the problem of finding a coalition structure maximizing the social welfare is tractable only when both k and the number of negative edges are constrained.
In many settings, agents exhibit skepticism in the presence of people whose beliefs radically different from their own, and they are reluctant to be persuaded by such individuals. We present a model of opinion dynamics where agents are receptive toward other agents that have similar opinions, but remain skeptical of agents holding disparate opinions.Loads of good (and relevant) comments about opinions in the human world:
Finally, we show that even skeptical agents are able to come to an early consensus and take co- ordinated action to reach a final opinion in most settings; but, agents in homophilic networks may fail to converge to a single opinion.
As agents are not guaranteed to share the same vocabulary, correspondences (i.e. mappings between corresponding entities in different ontologies) should be selected that provide a (logically) coherent alignment between the agents ontologies...(Very) informally: Alice asserts a belief - And Bob might accept or reject this belief bases on priori beliefs.
We formally present an inquiry dialogue and illustrate how agents negotiate by exchanging their beliefs of the utilities of each correspondence...
To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natu- ral language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first nego- tiation agent that successfully addresses this limitation.In a jobapplication scenario, negotiation issues might be :
In Aspiration Adaptation Theory (AAT) issues are addressed based on peoples typical urgency, or order of importance. If an agreement cannot be reached based on the value the human partner demands, the agent retreats, or downwardly lowers the value of previously agreed upon issues so that a ''good enough'' agreement can be reached on all issues.
Aspiration Adaptation Theory (AAT) is that certain decisions, and particularly our most complex decisions, are not readily modeled based on standard utility theory. For example, assume you need to relocate and choose a new house to live in. There are many factors that you need to consider, such as the price of each possible house, the distance from your work, the neighborhood and neighbors, and the schools in the area. How do you decide which house to buy? Theoretically, utility based models can be used. However, many of us do not create rigid formulas involving numerical values to weigh trade-offs between each of the search parameters. AAT is one way to model this and other similar complex problems.
will address how, and why, animals coordinate behavior. In many schooling fish and flocking birds, decision- making by individuals is so integrated that it has been associated with the concept of a ''collective mind''. As each organism has relatively local sensing ability, coordinated animal groups have evolved collective strategies th at allow individuals, through the dynamical properties of social transmission, to access higherorder capabilities at the group leve However we know very little about the relationship between individual and collective cognition.
I investigate the coupling between spatial and info rmation dynamics in groups and reveal that emergent problem solving is the predominant mechanism by which mobile groups sense, and respond to complex environmental gradients. This distributed sensing requires rudimentary cognition and is shown to be highly robust to noise.
We introduce Directions Robot, a system we have fielded for studying open-world human-robot interaction. The system brings together models for situated spoken language interaction with directions-generation and a gesturing humanoid robot. We describe the perceptual, interaction, and output generation competencies of this system. We then discuss experiences and lessons drawn from data collected in an initial in-the-wild deploymentThe robot is put out in the wild (in a Microsoft Office building):
Affective computing is the study and development of systems and devices that can recognise, interpret, process, and simulate human affects. In this context, computational modelling of emotion is a major challenge in order to design believable virtual humans.
...Here we propose to calculate the emotional dynamics within a multi-agent architecture. This mechanism is based on three dynamics: Event, temporal and external (Events impact the emotions depending on the internal state of the agent and its perception of the event).(As I understood it) Their verification found that their results was kind of on track and consistent with litterature about emotional contagion in groups.
A recent review of psychological studies has shown the existence of moderating factors of emotional contagion, such as social power or gender, which were simplified in this article. From the architecture viewpoint, these moderators should be included in the bodies (for individual moderators) and environment (for social moderators).Interesting...
Furthermore, we plan to replicate other psychological phenomena such as the impact of emotional contagion on cooperative decision-making, where the interplay with higher cognitive functions is more complex.
Users spend more time in communities where they have received social-psychological feedback, and in communities where they have previously invested more time. While behavior is stochastic, an analogy to humans playing mixed strategies in matrix games provides a simple and effective learning model in this setting.
|The understanding recruiter will use:||The demanding recruiter will use:|
|Narrow gestures||Spacing gestures|
|Positive facial expressions||Neutral facial expressions|
|Friendly gaze||Dominant gaze|
|Head tilting||Starring gaze|
|Convey interest||Convey neutrality|
Work that lays a preliminary foundation toward building a comprehensive gesture controller. The critical next step is to increase the expressiveness of the gesture controller so that the mapping learned by the speech-annotation mapping pro- cess can realize expressive gestures more tightly coupled to the uttered content.
we propose a multi-agent architecture that helps ease the process of recruiting patients for clinical trials. This paper presents a results from a deployment of the architecture, showing that it succeeds in recruiting a sufficient number of patients for multiple clin- ical trials.All pretty straight forward apparently, until we come to the legal issues...
Trust, reputation, norms and organisations are all relevant to the effective operation of open and dynamic multiagent systems.Luck started out by quoting Aaron Sloman: ''Computer Science is a sub-branch of Artificial Intelligence''.
Inspired by human systems, yet not constrained by them, these concepts provide a means to establish a sense of order in computational environments (and mixed human-machine ones).
In this talk I will review previous work across a range of areas in support of the need to develop theories and systems that provide the computational analogue of common social coordination mechanisms used by humans...
Societies and organisations should be concerned with:Agents find themselves in worlds where:
- The extent to which individual agents are willing to comply with norms.
- The effort expended to ensure norm compliance through enforcement and the severity of sanctions.
Individual agents should be concerned with:
- The achievement of individual objectives.
- The extent to which other agents are willing to perform any task resulting from interactions.
X-axis represents motivations:In simulations we would expect to see that ''Norm fails in the long run'' (1 million runs).
- Increase represents prevalence of malicious motivations, indicating that agents are more likely to defect if they see more utility in alternatives.
Y-axis represents organisations, norms and their enforcement:
- Increase indicates prevalence of stricter norms and enforcement
(Can constrain agent motivations malicious behaviour and prevent if intended).
Z-axis represents trust:
- Increase indicates increase in trust that agents place in others and increase in willingness to cooperate with others.
- No more distinction between natural and artificial systemsBut there is, of course, work ahead:
(We now work within a mixed system).
-So We need a science of electronic order.
The study of coordination and cooperation among agents has been at the heart of the multi-agent field since its inception. Since this early work, significant research progress and understanding about the nature of coordination has been made. Especially important has been the development of distributed constraint optimization (DCOP) and decentralized Markov decision processes (DEC-MDPs) frameworks over the last decade. These formal frameworks allow researchers to understand not only the inherent computational complexity of coordination problems, but also how to build optimal or near-optimal coordination strategies for a wide variety of multi-agent applications.I.e. DCOP amd DEC-MDP have provided a significant progress in understanding coordination.
Desire is the key connection to the agents creator, and the ultimate source of behaviour...I.e. Agents make choices baseed on utility, imitation, value sharing etc.
Agents continuously adapt their desires by means of both their intrinsic motivations, as well as a mimetic mechanism (as described in Rene Girardss theory). Agents acquire new goals not through fitness or novelty but out of mechanisms such as envy, imitation and competition...
Still, even if desires might not be a very rational concept, we want to treat the subject in rational way. According to Herbert Simon:
''Anything that gives us more information allows us to become more rational''.
We dont function as rational agents with the addition of some ''sociality'' modules to make us aware of other people. Rather we are social at the base and this sociality pervades all our reasoning, motivation, and any other aspect of our behavior.They continue:
As said before, just stating that agents are social because they have a communication language or can be programmed to work in a team does not make the agents social.So, what are the fundamental aspects of social individuals?
There are (at least) two issues that need to be investigated to accomplish this: 1. Allow for social motivations. i.e. motivations to reach a social rather than a practical goal.
2. Recognize that all actions have both practical and social effects that have to be modeled and accounted for.
(Agents) motives can be considered as being the core of ''energizing'' subsequent action. Besides the biological (homeostatic) motives, such as hunger and need for sleep (which are, in fact, not very salient in most of the social situations), McClelland distinguishes four motives.McClelland distinguishes four motives:
- AchievementIdentity - Agents want to be the same (belong), and yet be different...
- Affiliation motives.
- Power motives.
- Avoidance motives.
In general, motives are primary drivers that are always considered when a trigger arrives from the environment. Values are cognitive components that are considered when a cognitive choice has to be made about the course of action to follow.
Massive advances in network connectivity and increased affordability of computer hardware have recently led to a flurry of web-based applications that mediate interaction within human collectives. This has, in turn, led to an in- creased interest in ''collective intelligence'' applications.E.g.
Ridesharing applications where algorithms support travellers in collaborative route planning while also managing congestion in the urban areas involved; healthcare systems that monitor patients and their clinical treatment plans while prioritis- ing use of staff time and resources based on long-term data analysis; software development platforms that allow compa- nies to outsource production to teams of freelancers,Different from earlier systems, as these systems:
Embody a multi-perspective notion of hybrid man-machine intelligence, where the capabilities of humans and compuational artefacts complement each other (rather than ma- chines imitating human intelligence as in traditional AI).Importantly, such systems are continually co-designed by programmers and end users through human and machine contributions.
We model and examine the spread of information through personal conversations in a simulated socio-technical network that provides a high degree of realism and a great deal of captured detail. To our knowledge this is the first time information spread via conversation has been modeled against a statistically accurate simulation of peoples daily interactions within a specific urban or rural environment
Environment.The various areas might have little in common, except perhaps that these areas use models, where the notion of ''agent'' is beneficial.
- Plausible heuristics?Still, agents are in use, and is already influencing the world.
- Rationality assumptions (Game theory)?
- Historical observations of agent behaviour?
- History of system outcomes?
- Evolutionary stability concerns?
- Result of reinforcement learnings?
- Realistic agent based modellng calls for serious agent models.Questions from the audience initiated some final thoughts from Wellman:
- Boundaries between ABM (Agent Based Modeling) and MAS (Multi Agent Systems) are probably unnecessary.
- Agent behaviour assumptions are pivotal...
In the end we only care about the plausibility of the overall simulationAgents could run agent simulations themselves to decide what to do. So, where should we stop this complexity?
(SubParts within the model can be replaced).
La Ville Lumiere, Paris Pics.
Enactive Cognition Conference (Reading 2012) | Nasslli 2012 | WCE 2013 | CogSci 2012 | CogSci 2013 | Aspects Of NeuroScience 2017
About www.simonlaub.net | Site Index | Post Index | Connections | Future Minds | Mind Design | Contact Info
© October 2014 Simon Laub - www.simonlaub.dk - www.simonlaub.net - simonlaub.com
Original page design - October 20th 2014. Simon Laub - Aarhus, Denmark, Europe.