Who Needs Emotions The Brain Meets the Robot - Fellous & Arbib Part 18 doc

We now demon-strate how such a change in the emotional state of the agents would affect the best role allocation.. In Figure 11.4a, when all the agents were fearless, the number of scout

explicit, intended communication or by the intended actions they take in the world Further emotional signals are communicated across a variety of channels, verbally and nonverbally These channels vary in capacity, the specificity of the information effectively communicated, and the cognitive overhead in using them A person can smile at a cute baby without much thought but may need more resources to verbally express happiness Agent teams typically have two channels: communication and action These dif-ferences suggest potential benefits for using emotions in pure agent teams For instance, there might be an advantage to having agent teams communi-cate attitudinal or emotional information as well as an advantage to expos-ing this information to teammates automatically, through low-cost channels Consider building agents so that they could not only communicate and act deliberately after an accurate and possibly computationally intensive assess-ment of the state, but also emit some low-cost emotional signal based on an approximate state assessment For example, a robot could have hardwired circuitry that triggers light-emitting diodes that represent emotional cues like fear to indicate a state where the robot is in danger, worry to indicate low likelihood of success, and helplessness to indicate that it needs to help These emotional cues can be computed and transmitted quickly and could result

in the team being able to coordinate itself without having to wait for the accurate state estimation to be performed If, for example, agents could use these emotional cues to determine action selection of the other agents in the team, it could result in greater synchronization and, consequently, bet-ter teamwork

EXPERIMENTAL ILLUSTRATION

In this section, as an illustration of the effect of emotions on multiagent team-work, we demonstrate how the allocation of roles in a team is affected by emotions like fear Our approach is to introduce an RMTDP (Nair, Tambe,

& Marsella, 2003) for the team of agents, then to model the agents such that their emotional states are included

We now demonstrate how emotions can affect decision making in a team of helicopters To this end, recall the RMTDP analysis of TOPs men-tioned above The emotional state of the agent could skew how the agent sees the world This could result in the agent applying different transition, observation, or reward functions In this discussion, we will focus on how fear may affect the reward function used in the RMTDP For instance, in

a fearful state, agents may consider the risk of failure to be much higher than in a nonfearful state In the helicopter domain, such agents might

penalize heavily those states where a helicopter crashes We now demon-strate how such a change in the emotional state of the agents would affect the best role allocation

We consider a team of six helicopters and vary the number of agents that fear losing a helicopter to enemy fire These agents would place a heavy

penalty on those states where one or more helicopter crashed Figure 11.4a,b

shows the number of scouts allocated to each route (X-axis) as we vary the number of fearful agents in the team (Y-axis) from none to all six for two

different penalties for helicopter crashes In Figure 11.4a, when all the agents

were fearless, the number of scouts sent out was three, all on route 2; how-ever, when fearful agents were introduced, the number of scouts sent out changed to four, also on route 2, because the team was now prepared to lose out on the chance of a higher reward if they could ensure that each scout

that was sent out would be safer In Figure 11.4b, we reduced the amount

of penalty the agents ascribed to a helicopter crash When fearful agents were introduced, the number of scouts remained unchanged but the scouts now used route 1, a safer albeit longer route, instead of route 2, which was more dangerous but allowed the mission to be completed more quickly Thus, with the introduction of fear, we found that the team’s decision-making behav-ior changed such that the members either deployed more scouts or assigned the scouts to a safer route

Figure 11.4 Role allocations in fearful teams with different reward functions

Role allocations for reward function (a) Increasing the number of fearful

agents results in more scouts being sent together to increase the safety of the

scouting team (b) Increasing the number of fearful agents results in moving

scouts from a shorter but more risky route to a longer but safer route

0.

1.

2.

5

0 5 1 5 2 5 3 3.

Scouts on Route 1 Scouts on Route 3

Number of fearful agents

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Number of fearful agents

Scouts on Route 1

Scouts on Route 2

Although, the emotion “fear” was modeled simply as a penalty for states where a helicopter crashes, the purpose of the experiment was simply to show that emotional response affects what the team perceives is its best al-location In order to evaluate teams where emotions are represented more realistically , we would need the following:

• A more realistic model of how an agent’s emotional state would change based on new percepts This model of how the emotional state transitions can be incorporated as part of the transition function in the RMTDP model in order to evaluate the team’s performance in the presence of emotion

• A more realistic model of how humans (which the agents are simulating) would respond based on their emotional state This would form part of the TOP where the individual agent’s action selection is specified

Both the model of how emotional state changes as well as the model of human behavior in the presence of emotion should ideally be informed by human behavior in such task domains

CONCLUSION

This chapter represents the first step in introducing emotions in multiagent teamwork We examined the role of emotions in three different kinds of team: first, in teams of simulated humans, introducing emotions results in more believable agent behavior and consequently better simulations; second, in virtual organizations, where agents could simulate emotions to be more believable and engaging to the human and anticipate the human’s needs by modeling the human; and third, in pure agent teams, where the introduc-tion of emointroduc-tions could help bring in the same advantages that emointroduc-tions bring

to human teams

Teams of simulated agents and mixed human–agent teams can greatly benefit with computational models of emotion In particular, to evaluate and improve such teams, we would need the following:

• A model of how an agent’s emotional state would change based

on new percepts

• A model of how humans would respond based on their emotional state

Acknowledgment This research was supported by grant 0208580 from the

National Science Foundation.

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P A R T I V

CONCLUSIONS

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Beware the Passionate Robot

michael a arbib

12

The warning, “Beware the Passionate Robot,” comes from the observa-tion that human emoobserva-tions sometimes have unfortunate effects, raising the concern that robot emotions might not always be optimal However, the bulk of the chapter is concerned with biology: analyzing brain mecha-nisms for vision and language to ground an evolutionary account relat-ing motivational systems to emotions and the cortical systems which elaborate them Finally, I address the issue of whether and how to char-acterize emotions in such a way that one might say that a robot has emotions even if they are not empathically linked to human emotions.

A CAUTIONARY TALE

On Tuesday, I had an appointment with N at 3 P.M., but when I phoned his secretary at 2:45 to check the place of the meeting, I learned that she had forgotten to confirm the meeting with N I was not particularly upset, we rescheduled the meeting for 4 P.M the next day, and I proceeded to make contented use of the unexpected free time to catch up on my correspondence

On Wednesday, I decided in midafternoon to put together a chart to discuss with N at our meeting; but the printer gave me some problems, and it was already after 4 when I left my office for the meeting, feeling somewhat flustered but glad that I had a useful